Specialist Professional and Technical Services …...Geotechnical Asset Performance: Whole Life Cost Lot 1 SPATS Framework Specialist Professional and Technical Services (SPaTS) Framework,

Specialist Professional and Technical

Services (SPATS) Framework

Lot 1

Task 1-456

Geotechnical Asset Performance – Whole Life Assessment

Production of Task Findings Report

Work Package 5

April 2020

Geotechnical Asset Performance: Whole Life Cost Lot 1 SPATS Framework

Specialist Professional and Technical Services (SPaTS) Framework, Lot 1, Task 1-456


Specialist Professional and Technical Services (SPaTS) Framework, Lot 1, Task 1-456 1

Reference Number: 1-456

Client Name: Highways England

This document has been issued and amended as follows:

Version Date Description Created By Checked By Reviewed By

Received By

DRAFT 31/03/2020 For Comment

Louis Brazenell

Josh Hyde

James Batham

Sam Rogers

Elizabeth J. Simmonds / Alison Graham

David Wright

Angus Wheeler

FINAL 16/04/2020 Amended in line with HE comments

Louis Brazenell

Josh Hyde

James Batham

Sam Rogers

Elizabeth J. Simmonds / Alison Graham

David Wright

Angus Wheeler



This report has been prepared for Highways England in accordance with the terms and conditions of appointment stated in the SPaTS Agreement. Atkins Jacobs JV (AJJV) cannot accept any responsibility for any use of or reliance on the contents of this report by any third party.

Table of Contents

Glossary ................................................................................................................................................... 4

Definitions ................................................................................................................................................ 4

Introduction .............................................................................................................................................. 6

1. WP1 Condition and Purpose of SGMs ............................................................................................. 7

1.1 Introduction ..................................................................................................................................... 7

1.2 SGM inventory ................................................................................................................................ 7

1.3 SGM purpose .................................................................................................................................. 9

1.4 SGM condition ............................................................................................................................... 15

1.5 Consultation .................................................................................................................................. 20

2. WP2 Analysis of Performance of SGMs and General Earthworks ................................................. 23

2.1 Introduction ................................................................................................................................... 23

2.2 Existing information ....................................................................................................................... 23

2.3 Other asset owners ....................................................................................................................... 24

2.4 Task 1-266 condition indicators ..................................................................................................... 25

2.5 Slope hazard rating ....................................................................................................................... 30

3. WP3 Deterioration Models for SGMs and General Earthworks ...................................................... 32

3.1 Introduction ................................................................................................................................... 32

3.2 Probabilistic Modelling of Earthwork Deterioration (‘Bottom Up’ Approach) ................................... 32

3.3 Probabilistic Modelling of Earthwork Deterioration (‘Top Down’ Approach) .................................... 41

4. WP4 Development of Network Risk Areas for all asset types ........................................................ 47

5. Conclusions and recommendations ............................................................................................... 49

Appendix 1 – ‘Bottom Up’ Matrix............................................................................................................. 53

Appendix 2 – ‘Bottom Up’ Figures .......................................................................................................... 57

Appendix 3 – ‘Top Down’ Methodology Plots .......................................................................................... 60

Figures

Figure 1-1: Confidence rating flow chart to show how the confidence rating for each source of data is derived ................................................................................................................................................... 13 Figure 1-2: Comparison of number of SGMs with coincident defects between verified defective SGMS . 17 Figure 1-3: The percentage of total network defective SGMs ................................................................. 18 Figure 2-1: The length of classified observations within each condition grade ........................................ 27 Figure 2-2: Slope Hazard Rating (Hazard assessment note: 2017) ........................................................ 30 Figure 3-1 - Pore water pressure coefficient (ru) distributions ................................................................. 36 Figure 3-2 - Cohesion, c’ (kPa) distribution ............................................................................................. 37 Figure 3-3 - Phi (ɸ’cv), (degrees) distribution .......................................................................................... 39 Figure 3-4 - Top Down Approach............................................................................................................ 43 Figure 3-5 – Glossary of GAD extract field codes referenced in this document ....................................... 44 Figure 5-1: Aspect of each framework being taken forward to develop overall condition grade .............. 49



Tables

Table 1-1 - The number of SGMs identified for each purpose. ................................................................ 14 Table 1-2 – The number of SGMs identified by each source. ................................................................. 14 Table 1-3 - Common defects by SGM type ............................................................................................. 19 Table 1-4 - Summary of consultation with asset owners. ........................................................................ 21 Table 2-1: Task 1-266 Condition Indicators related to Class and Location Index .................................... 26 Table 2-2: Description of each Condition Grade from Task 1-266.......................................................... 26 Table 2-3: Pivot table extract of condition grade length for each earthwork. ........................................... 27 Table 2-4: Count and length of observations assigned a condition grade ............................................... 27 Table 2-5: Summary of earthworks condition based on recorded worst case condition of observation ... 28 Table 2-6 – Task 1-266 Ratio result. ....................................................................................................... 28 Table 2-7: Ratio of individual observation condition to total asset length. ............................................... 28 Table 2-8: Ratio of worst-case condition to asset length ......................................................................... 29 Table 3-1 - Slope geometry combinations to be analysed ...................................................................... 34 Table 3-2 - Pore water pressure coefficient (ru) values derived for Slope/W analysis.............................. 35 Table 3-3 - Cohesion (c') values derived for Slope/W analysis ............................................................... 36 Table 3-4 - Phi (ɸ’cv), (degrees) values derived for Slope/W analysis ..................................................... 38 Table 3-5 – Ground Model Summary (Global Distribution)...................................................................... 38 Table 3-6 – Volumes of data from joins .................................................................................................. 42 Table 5-1: Summary of constraints associated with ‘bottom up’ methodology to be considered if applying to the wider SRN. ................................................................................................................................... 50



Glossary

GAD Geotechnical Asset Data

HAGDMS Geotechnical Asset Data Management System for Highways England

LUS M25 Later Upgraded Sections

ORR Office of Road and Rail Regulator

SGM Special Geotechnical Measure

SPaTS Specialist Professional and Technical Services

SRN Strategic Road Network

Definitions

Project Specific Definitions

• Special Geotechnical Measure (SGM) - These are measures over and above general earthworks

construction required to; mitigate geotechnical risk associated with ground related hazards or

remediate geotechnical defects that may have resulted from the presence of geo-hazards. Similar

techniques implemented to facilitate widening or other improvements are, for the purposes of this

task, also classified as Special Geotechnical Measures.

• Structured Data - Structured data refers to data held within fixed fields within a defined database

hierarchy. For the purpose of this task, this term relates to the Geotechnical Asset Data (GAD)

held in HAGDMS. This includes all location and detailed information relating to earthworks and

features held within the database.

• Unstructured Data - Unstructured data refers to data held without a pre-defined data model,

typically not in a database format, but can also refer to free text in a database. For the purposes

of this task, this term relates to the information which is embedded within PDF reports held in

HAGDMS. Limited structured information associated with each electronic report is held by

Highways England.

• SGM Catalogue – The SGM catalogue refers to a list of 99 unique types of SGM that can be found

on the Highways England network.

• SGM Inventory – The SGM inventory refers to a list of 11853 individual SGMs that can be found

on the Highways England network.

• Condition – For the purposes of this task, condition will be directly related to the observations

recorded in accordance with HD41/15 on each of the earthwork assets.

• Performance – For the purposes of this Interim Summary Report, performance is the ability of the

asset to perform as anticipated, and at this stage is analogous to condition, until such time that the

understanding of anticipated behaviour is further developed.

Associated Highways England Tasks

• Task 416 Review of Geotechnical Asset Data – Completed task which aimed to identify locations

and condition of SGMs across the Highways England network, utilising structured and unstructured

data as it is held within HAGDMS. The recommendations and output from this task were the basis

of the aims for Task 594.

• Task 594 Strengthened Earthworks – Completed task which followed on from Task 416. This

task looked to further refine the process of identifying SGMs on the network and more efficiently

identify reports which relevant information could be found within. The task also looked at identifying

the condition of SGMs across the network. The processes established during this work were taken

forward for task 1-456.



• Task 197 Slope Geotechnical Hazard Rating (2014) – Completed task which aimed to provide a

rating to each earthwork asset based on the weighted length of defects recorded on cohorts of

similar assets. This was a strategic task which was aimed to be high level and not replace the

need or understanding of assets at an individual basis. The outputs and processes were further

refined in 2017.

• Task 266 Condition Indicators – Ongoing task which aims to develop condition indicators which

will act as a supporting metric to be implemented into RIS2 (2020-2025) as a way of supporting

the overall KPI against “Keeping the Network in Good Condition”.



Introduction

Task 1-456 builds upon the completed Task 594 whilst using data mining methods detailed in Task 416.

This task continues from Task 594 by identifying and recommending further data capture improvements, assessing asset performance and progressing the development of earthwork deterioration models. This will support the assessment of the long-term behaviours of general earthworks, and SGMs.

Through collaboration with other work streams, this work will contribute to the overall goal of providing a planning and prioritisation tool for Highways England to strategically manage network risk and asset resilience.

The scope of the work will include the following summarised work packages. These activities include the derivation of a number of methodologies and frameworks which can then be applied for subsequent data analysis.

Work Package 1 entails gathering and reviewing updated GAD from HAGDMS, with subsequent analysis to update the national catalogue of SGMs, whilst establishing their current condition and purpose.

Work Package 2 involves liaison with a number of parties, including Highways England, to understand and agree how the expected performance of all geotechnical assets is assessed, measured and benchmarked. The task includes investigation of how other asset owners do this and what lessons can be learnt from them.

Work Package 3 aims to establish deterioration models that can be applied to Highways England Earthworks Assets on a national basis. However, due to the experimental nature of this work, it is intended to first prove the concept of the whole process required to apply this technique including; sourcing and analysing available information on parameters and variability, conducting the probabilistic analyses and subsequently developing a process to consistently apply the outputs to individual earthworks assets.

Work Package 4 uses the outputs of the condition and deterioration assessments to identify high risk earthwork cohorts, and high interest SGMs. These are of importance for managing specific high-risk geo-hazards, or where consequences of failure are significant with little or no pre-failure indicators of deterioration. Regular liaison with other Resilience tasks, quarterly or as and when required has taken place.

Work Package 5 comprises the production of this Final Task Summary Report presenting the outcomes of the tasks, limitations, and recommendations to be taken forward for future consideration.

Work Package 6 entails the production of Digital Data Sets which comprises the development of the existing GIS data set to include additional attributes defined by this task, such as condition and engineering characteristics.



1. WP1 Condition and Purpose of SGMs

1.1 Introduction

As part of Task 1-456, Work Package 1 entails the gathering and reviewing of updated GAD from HAGDMS, with subsequent analysis to update the national inventory of SGMs, whilst establishing their current condition and purpose.

As part of this work package, consultation with the wider AJJV and other asset owners has been undertaken to understand specific SGM types of interest. Related to both condition and performance of these, this will allow a greater understanding of which SGMs are typically problematic for asset owners and managers under a variety of conditions and ages.

The outputs of this Work Package assist with establishing potential performance trends for each SGM and understanding the needs for monitoring and potential for geo-hazard mitigation as part of the overall assessment of network resilience.

1.2 SGM inventory

Aims and objectives

To understand the current condition and potential purpose of SGMs, an updated SGM inventory was required to have an accurate understanding of what SGMs currently exist on the network and where they are located. Therefore, the aim of this sub-task was to produce an updated SGM inventory that can be used for subsequent sub-tasks and for use by other task teams that require SGM information. This SGM inventory would utilise methodologies developed in previous Tasks.

Data sources

An extract from HAGDMS was received on 14 May 2018, which comprised a full extract of the GAD, as held on HAGDMS on 28 March 2018, this extract formed the structured data. To accompany this, a hard drive was received, from HAGDMS, on 2 July 2018 which contained all of the reports held on HAGDMS as of 28 June 2018, this was followed by an index of these reports on 13 July 2018. The hard drive of reports and associated index comprise the unstructured data used as part of this Task.

Development

Structured data

For analysis of the structured data, the methodology developed as part of Task 594 has been applied to the data which was identified as being captured after the date of the previous GAD extract (22 May 2015). The structured data was analysed using Microsoft Access. A number of queries were created within the software to interrogate the information.

The majority of these queries were for SGM identification. These were searches that interrogated the specific fields where SGMs are most likely to be recorded. Multiple queries were used due to file size limitations within the software. These queries were run sequentially to generate a comma-separated list of SGMs contained within each observation. The queries used were ones created, checked, and reviewed as part of Task 594 and therefore established terminology and search functions had already been created which enabled compatibility with the results from Task 594.

Once the search queries had been run and the comma separated list of SGMs had been extracted into Excel, this output was combined with the Task 594 output and the geotextile tick box output, this was included to create a more robust dataset. In Excel, a number of macros were executed to refine the data.



These macros were based on those used as part of Task 594. However, improvements were made in line with the recommendations from that Task, including:

• Inclusion of GETX1 – During Task 594 the search functions did not include records where the tick box for geotextiles had been selected in the GAD data. Including this criteria has resulted in an increase in geotextiles being recorded in this analysis.

• Automation of metallic reinforcement reclassification – in Task 594 several SGMs initially identified as rock netting / mesh were manually changed to metallic reinforcement after identifying this to be the most appropriate category. As part of this task, the Excel macros have been updated to apply this improved categorisation.

The combination of macros that had been developed helped to refine the raw data in the following ways:

• Data cleansing – Removal of SGM records that only identified Filter Drains as, in isolation, these are not classed as an SGM. These records have, however, still been catalogued where they exist with another SGM as they may influence the ability of the SGM to perform as designed.

• Duplicate SGMs were removed where records of the same occurrence had been established from different sources e.g. tick boxes and observation descriptions.

• Creating unique records per SGM – The initial output contains a combined list of SGMs identified within a single observation. However, to aid analysis, a process was developed to generate a record per SGM.

• Coincident classified features – It was identified that establishing coincident classified features with SGMs may assist in understanding their condition and performance. As a result, where present, coincident classified observations were identified for each SGM record.

Following execution of the Excel macros, two outputs were created from the structured data:

• a list of all unique SGMs, known as the SGM Inventory, and

• a list of all Observation ID’s and the SGMs associated with them.

The methodologies used were consistent with those developed during Task 594 to maintain uniformity between the datasets. At the end of Task 594, the SGM Catalogue was shared with NBS to aid in the development of the Uniclass 2015 classification system. It is proposed that, at the end of the current Task, the updated SGM Catalogue will again be shared with NBS to assist in the development of a consistent classification system for civil engineering products and systems.

Unstructured data

For the unstructured data, the methodology developed as part of Task 594 has been applied to the data which was uploaded to HAGDMS since 28 September 2015. A list of new reports was generated by cross referencing the reports index with both the reports analysed as part of Task 594 and the reports on HAGDMS. Once this list of reports had been created, a filter of “relevant” report types was applied. These are reports that are likely to contain information regarding SGMs.

Once the reports had been filtered by type, the report ID’s that were deemed to be ‘relevant’ formed a report index that was uploaded into the dtSearch software. Within the dtSearch software, terminology that was established as part of Task 594 was used to identify ‘hits’ of SGMs. These could either be positive hits, reference to the presence of an SGM, or negative hits, reference to the absence of an SGM. These hits were then manually checked to validate that the positive ones were correct. These checks demonstrated an accuracy of 90%+ for this method of SGM identification.

The output from the analysis of the unstructured data was a list of reports and the relevant SGMs that had been manually verified as part of this process.

Limitations

• Data accuracy - The main limitations this task faced were due to the quality and consistency of the GAD data available, as the GAD data inherently contains human error in the form of spelling



mistakes and incorrectly input data (such as mis-selected tick boxes). This has been addressed to some extent by including some common spelling mistakes in the search terms. However, it is likely that the quality of the input data will have impacted the accuracy and completeness of the outputs.

• Duplication of SGMs – Due to the search terms used to establish the presence of SGMs within the structured data, it is possible that two records of the same occurrence of an SGM may be produced. This could occur where the same SGM is referred to using different terminology across several observations. Common examples of this are non-specific retaining walls (NSRW), these are the most frequent SGM picked up in the structured data, however they are often referring to a more specific retaining structure that is mentioned in other observations of the same SGM. Another example of this was found with sheet pile walls and PVC retaining walls; as there is only a GAD tick box for ‘sheet pile wall’, this is often ticked when observing a PVC retaining wall and so would result in a duplication. In these instances, a macro has been used to identify where there is a duplicate SGM type within the same earthwork and extents giving the ability to remove them. Some SGMs however, may be classified differently dependent on the interpretation of the inspector, for example, where a block retaining wall and concrete retaining wall have been observed on the same earthwork and extents, it is likely referring to the same SGM, but without looking in detail at these observations it is not possible to say which one is more accurate.

• Size of database – Due to the quantity of observation records in the dataset, queries on the full dataset exceeded the 2 GB limit that applies to Microsoft Access. Therefore, to process the data it had to be split. This was achieved by querying all data that post-dated Task 594 in Microsoft Access and recombining these results with the outputs produced from Task 594 to create an updated set of results.

• Variation in terminology - As part of the text identification process applied to both the structured and unstructured datasets, SGMs are identified by searching key words and context phrases. Reports, observation descriptions, and Form C’s were searched as part of this process. The limitation is that a range of terminology could be used for the same SGM and, although efforts were made to identify common variations, not all of these similarities may be captured.

• Combining data – Analysing Task 594 data, geotextile tick box data, and all data captured since Task 594 separately, and combining the results together, has resulted in a number of changes to the derived SGM extents where new data has been recorded which supersedes or changes the previously derived information. Thorough checks have been conducted on the data set and it is recommended in the future that all data be analysed together, this will therefore require the current database size limit to be increased.

• Report types – To improve the search results from unstructured data, “relevant” report types were established. These are reports that only relate to the design and construction phases of geotechnical schemes and are therefore more likely to contain information relevant to SGMs. It is possible that reports produced earlier in the certification process may uniquely reference some SGMs. However, the number of instances where this is the case is considered to be very limited.

1.3 SGM purpose

Aims and objectives

It is important to understand why SGMs have been constructed on the network, as this improves:

• understanding of previous performance issues (prior to SGM installation)

• appreciation of how performance issues may be expressed (and hence requirements for effective monitoring)

• identification of patterns in SGM performance



• evaluation of risk relating to underlying geo-hazards

The SGM inventory includes a column that gives a potential purpose for each SGM, together with a confidence rating based on how the recorded purpose has been derived. The main three types of purpose an SGM would serve is; repair, improvement, and hazard mitigation. It should also be noted that this task refers to recorded visible SGMs and that buried SGMs are not part of the task due to identification/validation constraints.

Data sources

An extract from HAGDMS was received on 14 May 2018, which comprised a full extract of the GAD, as it was held on the HAGDMS on 28 March 2018. To accompany this, a hard drive was received on 2 July 2018 which contained all the reports on HAGDMS as of 28 June 2018, this was followed by an index of these reports on 13 July 2018.

Following the issue of the SGM inventory to Mott MacDonald on 28 February 2019, Mott MacDonald provided the results of a spatial query which identified the geo-hazards present at the locations of the SGMs provided. This query was run against the hazard maps that are being created as part of Task 1-532 Enhanced Hazard Products.

A matrix was created that associated each SGM type to one or more high-level purpose. This matrix also captured which types of hazard each SGM would commonly be installed to mitigate against. This matrix was then utilised in the hazard mapping to identify where SGMs and hazards are co-located and if the SGMs present would mitigate for that particular hazard. The matrix was then also used if a purpose couldn’t be defined through any other sources then their high-level purpose would be applied to them.

Development

Previous work was carried out during Task 594 to establish potential purpose of SGMs and was developed using structured and unstructured data. This involved searching for terms within the observation description, Form C description, and the small number of associated reports that were reviewed in detail to establish a likely purpose of an SGM.

This methodology was developed further during this sub-task. The potential purposes of SGMs are:

• Network Improvement (Generic) – This is identified through the report title search and the high-level purpose matrix. This searches for generic text such as ‘improvement’ and related derivations of the term.

• Improvement (Widening) - This is identified through the observation description and Form C text. This searches for generic text such as ‘widening’ and any related derivations of the word.

• Improvement (Communications) – This is identified through the observation description and Form C text. This searches for generic text such as communication, VMS, CCTV, lighting column, and any related derivations of these terms.

• Defect Repairs – This is identified through the observation description and Form C text search, the report title search, and the high-level purpose matrix. This identifies words such as fix, repair, renew, reconstructed, and any related derivations of those words.

• Hazard Mitigation – This is identified through the interpretation of the geo-hazard spatial query that was performed by Mott MacDonald. This identifies instances where a hazard which has a sufficiently high likelihood of being present, and an SGM which may be used to mitigate the same hazard, are co-located.

The sources of data identified as being useful to provide potential SGM purposes, are:

• Observation descriptions and Form C’s – This is information within the structured data that is

extracted from HAGDMS. Terms were established to search the descriptions of these to find

indications of SGM purpose.



• Titles of associated reports – This is part of the unstructured data. By analysing the titles of the

reports found on HAGDMS, a likely purpose can be established for reports held on the system.

Where this report is associated with an SGM, it has been assumed that their purposes are the

same.

• Task 1-532 hazard mapping – From collaboration with Mott MacDonald, outputs from Task 1-532

(Geotechnical Enhanced Hazard Products) indicated whether a geohazard was co-located with an

SGM. An assessment was then made to check if the SGM would mitigate that hazard, and whether

that hazard had a high enough likliehood of occurring that an SGM would be installed.

• High-level purpose matrix – For all the identified SGMs a high-level purpose matrix has been

produced identifying the potential purposes of an SGM, based on engineering judgment.

Observation descriptions and Form C’s

The HAGDMS extract was searched to identify relevant terms within the observation descriptions and Form C’s. This expanded on the method used as part of Task 594 by differentiating between improvement (widening) and improvement (communications), previously these would both have been identified as improvement.

Titles of associated reports

Using the titles of associated reports from the report index, search terms were established and used to search the titles of reports. This established a potential purpose for each report. If the report had an associated SGM, the purpose of the report was also applied to that SGM. Overall, a total of 68 individual reports were associated with SGMs, this led to 106 out of the total 11853 SGMs having their purpose identified through this method. Although this is a relatively small number, due to the small proportion of SGMs having associated reports.

Task 1-532 hazard mapping

Mott MacDonald were provided with the SGM inventory created as part of this work package. From this, a geospatial query was run against each category of hazard defined as part of Task 1-532. This geospatial query confirmed whether an SGM was co-located with one of the identified geohazards. The output provided by Mott MacDonald listed a range of hazards with likelihood rating against each individual SGM. These hazards include:

• Shrink-swell susceptible soils;

• Natural landslides;

• Dissolution;

• Compressible/collapsible ground;

• Coal mining;

• Non-coal mining;

• Brining; and,

• Landfills.

A hazard being co-located with an SGM does not explicitly mean the SGM has been constructed to mitigate that hazard. A hazard matrix was developed which lists each unique SGM type likely to be utilised to mitigate each of the hazards identified as part of the Task 1-532.

The Task 1-532 output includes an assessment of the likelihood of a given geohazard being present. After assessing the Task 1-532 source material, and in agreement with Mott MacDonald, it was determined that an SGM solution is only likely to be installed, except in rare cases, for high/very high likelihoods of hazards occurring. Therefore, if a high/very high hazard was present with an SGM that has been identified to mitigate for that hazard, then the potential purpose of the SGM has been suggested as hazard mitigation.



Purpose matrix

A method for determining potential purpose of SGMs is through a purpose matrix. This lists all the SGM types and, based on engineering judgement, records one or more purpose for each SGM type.

Confidence Rating for SGM Purpose

Once potential purposes had been determined, a Confidence Rating was allocated to each source of this information. Where more than one information source is available, the one with the highest Confidence Rating will be used to assign the purpose to an SGM. This order was:

• Observation description and Form C’s – These sources have the highest confidence rating, as they relate to data captured for individual observations and are location-specific.

• Report titles – These reports are also linked to observations and therefore are likely to provide accurate information to determine the potential purpose of an SGM. However, they may also report historic SGMs encountered during the works.

• Hazard mapping – This is determined through the co-location of geohazards and SGMs that may mitigate for that hazard. There is no other specific information that confirms the purpose, and a lower confidence is assigned as it is inferred.

• Purpose matrix – The purpose matrix is the source with the lowest confidence as it relates to typical applications for each type of SGM.

The decision process used to assign the Confidence Rating is illustrated in the flow chart below Figure 1-1.



Figure 1-1: Confidence rating flow chart to show how the confidence rating for each source of data is derived



From the activities in this sub-task all SGMs were assigned a purpose. The number of SGMs with each purpose is displayed in Table 1-1. The purpose for each is detailed in the SGM inventory.

The results from this sub-task show a large proportion of SGMs have multiple potential purposes. This arises where there are multiple search terms suggesting different purposes, or a lack of information prevents the purpose from being identified.

Table 1-1 - The number of SGMs identified for each purpose.

Purpose Count Percentage

Repair 1855 15.7

Improvement 131 1.1

Improvement (Comms) 624 5.3

Improvement (Widening) 159 1.3

Hazard Mitigation 3275 27.6

Repair, Improvement 291 2.5

Repair, Improvement (Comms) 76 0.6

Repair, Improvement (Widening) 22 0.2

Hazard Mitigation, Repair 3 0.0

Hazard Mitigation, Improvement 22 0.2

Hazard Mitigation, Repair, Improvement 5395 45.5

Total 11853 100

The amount of SGMs identified by each source is shown in Table 1-2.

Table 1-2 – The number of SGMs identified by each source.

Source Count Percentage

Observation Description 2715 22.9

Report Title 61 0.5

Hazard Mitigation 3185 26.9

Purpose Matrix 5892 49.7

Total 11853 100

Limitations

• Data accuracy – One of the main limitations associated with this task is the quality and consistency of GAD data available, as the GAD data inherently contains human error in the form of spelling mistakes and incorrectly input data. This has been addressed by including some common spelling mistakes in the search terms to encompass a range of vocabularies that would help to identify purpose.

• Limited numbers of SGMs with associated reports – A limitation for determining the purpose of SGMs from reports is that this method is limited to only those SGMs that have associated reports. This reduces the number of SGMs for which a purpose can be identified.

• It is assumed that the purpose of an associated report correctly describes the reason the SGM was constructed e.g. widening. As part of SGM purpose identification, search terms were used against the titles of all the reports provided as part of the reports index from the unstructured data task. There may be isolated cases where the SGM purpose differs from the purpose determined from the report title (e.g. a widening scheme report is associated with an SGM, but the SGM was to repair a defect which was carried out as part of the widening scheme).



• Data mining methods – Data mining techniques used to identify SGM purpose based on terms contained in the observation descriptions. There may be isolated occasions where the SGM purpose had been identified as a repair, but the observation description contains additional information confirming the SGM in question was ‘not a repair’. These isolated occurrences cannot be avoided without extensive manual intervention.

• SGMs having multiple applications – It is possible for multiple purposes to be identified for an SGM, depending on the nature of the related information available. This can occur either through text recognition identifying terminology that defines more than one purpose, or through a lack of information meaning that the potential purpose cannot be refined.

Conclusion

The work within this sub-task has built upon the SGM purpose methodology that was created during Task 594. This development has now allowed one or more potential purposes to be assigned to each SGM. A Confidence Rating has also been developed and applied, to allow other users of the data to easily understand the reliability of this information.

It is evident from the results that only 23% SGMs have a purpose recorded within the observation description or Form C. Almost half of all SGMs had no specific purpose information associated with them and have therefore been assigned a purpose from the matrix, based on engineering judgement. To improve the current situation, it will be important to capture this attribute in future through assessment as part of Principal Inspections, including this in as-built data requirements, and capturing tacit knowledge from teams in the Maintenance Areas. This will be particularly important in relation to hazard mitigation and improving Highways England’s understanding of where hazards with no recorded mitigation may exist.

1.4 SGM condition

Aims and objectives

An aim of this Task is to use the available data to understand the condition of SGMs across the network. This will inform Highways England whether SGMs are performing as expected, help identify trends in performance and also support the identification of network areas potentially at risk. Therefore, by understanding the condition of SGMs we can help understand the performance and potential longer-term liability of the earthworks asset portfolio.

Data sources The extract from HAGDMS received on 14 May 2018 was used in conjunction with the SGM inventory.

Development

As part of Task 594 the condition of SGMs had been initially interpreted by identifying geospatially coincident defects. After further detailed review of these defect observations it was apparent that not all of these coincident defects could be assumed to reflect the condition the SGM. Due to this, further work was carried out to assess in detail all the coincident defects for each SGM type and determine how many of these actually represented a defect affecting the SGM.

Using the SGM inventory and HAGDMS extract, a cross referencing exercise was completed to retrieve all defect observation descriptions for each recorded visible SGM type. A manual check was then undertaken to identify whether each observation related to the condition of the visible SGM. This activity provided a better understanding of the number of visible SGMs that are defective, and the ability to further investigate common characteristics and trends relating to the performance of SGMs.

Figure 1-2 shows a comparison between the number of SGMs with coincident defects with the number of verified defective SGMs, it is worth noting the vertical axis is capped to 200. Filter drains have a total of



647 SGMs with coincident defects. Figure 1-3 shows the percentage of defective SGMs compared to the total number of SGMs. In total 2048 of 11853 SGMs have coincident defects, however, only 361 of 11853 SGMs have been verified as being defective, based on a coincident defect.



Figure 1-2: Comparison of number of SGMs with coincident defects between verified defective SGMS



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PERCENTAGE OF VERIFIED DEFECTS COMPARED TO NUMBER OF SGMS

Figure 1-3: The percentage of total network defective SGMs



Following the validation of coincident defects which relate to SGMs, a task was undertaken to understand if there were common defects for each SGM type. From this task, it was determined that only 20 SGM types had a defect that was common across multiple observations, this is shown in Table 1-3.

Table 1-3 - Common defects by SGM type

Limitations

• Data accuracy – One of the main limitations associated with this task is the quality and consistency of GAD data available, as the GAD data inherently contains human error in the form of spelling mistakes and incorrectly input data. This has been addressed by including some common spelling mistakes in the search terms to encompass a range of vocabularies that would help to identify condition. The assessment is also limited to the data as it is captured on site and the way in which classified features are associated to SGM observations.

• SGM/Defect Visibility – this task is limited in only being able to assess recorded visible SGMs and defects due to the use of GAD data collected during Principal Inspections.

Conclusion

It is evident that the currently available data, as recorded in HAGDMS, does not support a direct and accurate assessment of whether defects recorded at the location of an SGM actually relate to the SGM or other components of the earthwork asset. It is also apparent that making an assumption that coincident defects do indicate the condition of an SGM can significantly overestimate potential condition problems with SGMs. Through the validation process, it has also been confirmed that overall the number of visible SGMs which are recorded to be defective is relatively low (361 of 11853 SGMs, i.e. 3%). It is not known what percentage of buried SGMs are defective due to obvious inspection constraints.

SGM Type

Condition related descriptions Common defect Count

Common defect Count

Block Wall (BLCW) 58 Cracking 14 Counterfort Drain (CFDR) 10 Burrowing 2

Mass Concrete Wall (CNCW) 30 Cracking 7

Crest Drain (CSDR) 6 Flooding/ponding 3

Rock Catch Fence (DBFN) 3 Full debris fence 2

Erosion Mat (ERSN) 7 Matting slipped 3

Filter Drain (FILT) 75 Seepage 20

Geogrid (GEGD) 65 Burrowing 27 Cut/Tear 27

Geotextile (GETX) 15 Burrowing 8

Grout Injection (GROT) 4 Cracking 3

Metallic Reinforcement (MTLK) 4 Burrowing 2

Non-Specific Anchor (NANC) 6 Undermining 5 Non-Specific Retaining Wall (NSRW) 28 Distortion/deformation 6

Regrade (REGD) 7 Tension crack 2 Slip 2

Rock Fill (ROCF) 4 Terracing 2

Slope Drain (SLDR) 40 Seepage 17

Rock Netting / Mesh (SMEH) 10 Cut 3

Stone Wall (STNW) 26 Collapse 13

Toe Berm (TOBR) 6 Slip 4

Toe Drain (TODR) 35 Subsidence 6



The outputs of the task identify that there are a small number of visible SGMs with a high percentage of recorded condition problems. As shown in Figure 1-3, 44% and 33% of buttresses and scaled earthworks respectively, have been recorded as defective. It should be noted however, that the total number of these SGMs recorded on the network is small, so the associated proportions may not be statistically significant. It is also recognised that scaling is a periodic activity and, as such, it may be useful to capture the dates when this activity is undertaken to assist in forecasting future requirements.

For the remaining visible SGM types, less than 15% of each type are recorded as being defective, with 10 No. SGM types recorded as have more than 10% with defects, including buttresses and scaling.

Overall, the task has shown a very low percentage of the network has defective visible SGMs present (361 of 11853 – 3% of recorded visible SGMs), therefore indicating that in general the network SGMs are in relatively in good condition.

What can be further concluded from a review of the 361 defective SGMs, from Figure 1-2 there appears to be a larger number of the following defective SGMs; filter drains (56), block walls (38), slope drains (32), toe drains (26), non-specific retaining walls (26), geogrids (23), mass concrete walls (22), gabion walls (20), and stone walls (15). This would indicate that most defective SGMs are related to drainage and structural retaining walls, as well as a larger number of defective geogrids. For all SGMs the length of defects varies, defects range from being 1m in length up to and exceeding 500m in length. Despite these large variations seen across most SGM types the average length of defect typically lies between 30m to 60m.

Currently, there is no consistent way of determining the cause of these defects, whether that be from natural deterioration, vandalism, environmental damage, poor construction quality etc. As a result, it is recommended that on future inspections where possible the cause of defects should be stated in observation descriptions. Consideration could also be given to updating the GAD data fields in future to capture this information in a more structured format. If the cause of defect is not obvious, this should be also recorded. This will allow future analysis of GAD data to identify potential trends between SGM types at why defects are occurring.

As part of our collaboration with Task 1-447 Proactive Monitoring, a summary of the common defect types, and the physical changes typically associated with these defects, will be provided. This will assist Task 1-447 in understanding the typical defect mechanisms associated with specific SGMs and will allow correlations to be made with the ability of various monitoring techniques to detect these changes.

It is proposed that the information derived from this task is used to develop a ‘best practice’ note for those carrying out principal inspections to help improve the quality and relevance of information captured in relation to the presence of SGMs, their condition and the nature of any associated defects together with potential causes.

The activity to assess defect characteristics has been successful in identifying common issues. These will be helpful in future, to be considered in design and potential maintenance activities. Whilst most of the characteristics are as we may expect, what it also does highlight is where SGMs may have been in a context that we had not anticipated and may therefore require further clarity to the definition of SGMs and also potential new SGM categories to be established.

1.5 Consultation

Aims and objectives

An important aspect of this Task was to identify and assess SGM types that may be of concern or particular interest to Highways England and their supply chain, other asset owners, and the wider SPaTS task teams.



Though consultation with interested parties, the task aimed to identify problematic/vulnerable SGMs, common condition indicators, and performance issues. By comparing the results of this consultation with the SGM condition and performance information, it is possible to identify commonalities and differences between perceived and recorded condition and performance.

Development

To understand which SGMs were identified as being problematic or vulnerable by consultees, a number of activities were identified to collect information. These included collaboration with other resilience task teams, collecting data via questions and discussions at supply chain events, and consultation with the wider industry including; technical experts, asset owners, and academics.

In October 2018, a workshop was held with Highways England Operational supply chain where Task 1-456 was represented. Feedback was obtained from those present regarding SGMs which are viewed as being of concern within different regions of the network.

Consultation was undertaken throughout March and April 2019 with the AJJV and other asset owners, such as Transport Scotland, Transport for London, London Underground, Network Rail etc to further develop the understanding of which SGMs are of significance for other owners. This consultation helped to develop an understanding of condition and performance of SGMs in different environments and could potentially focus mutually beneficial research in future. The results of these consultations are shown in Table 1-4.

Table 1-4 - Summary of consultation with asset owners.

Asset owner SGMs of concern Cause of issues

Welsh Government Soil nails, gabions, geotextiles, drainage

Poor design and unsuitable ground conditions. Environmental impacts (i.e. heavy storms)

Network Rail

Soil nails, lime stabilisation, and electrokinetic stabilisation

Most issues are around installation

Transport for London

Counterfort drains, drainage, sheet piles, and other pile based retaining walls

Maintenance of drainage assets and build up of water behind retaining walls

Various AJJV Soil nails, gabions, and drainage

Mainly related to waterlogging and poor construction

Further collaboration has been undertaken with Task 1-447 Geotechnical Asset Performance: Pro-active performance monitoring, and this will continue throughout the remainder of this task.

Conclusion

From this work, SGMs such as soil nails and rock bolts were perceived to be of particular concern, with regards to performance, as well as sheet pile walls due to potential corrosion. The need for further research and information in these areas is supported by the CIRIA project “Grouted anchors and soil nails – condition appraisal and remedial treatment”. There are currently only three records of defective soil nails on the network however due to poor testing/maintenance records and being a primarily buried SGM, it is difficult to quantify defective soil nailed locations.

There are also a number of buried SGMs that are increasing in usage, such as geosynthetic reinforcement, which do not have established efficacy research over their design life. Therefore, there is a need for better understanding of their condition and performance over their life cycle. This is important as geogrids were one of the SGMs that have been flagged as having a higher number of defects recorded.

What was also evident from the consultation was that there are relatively few SGMs which are a problem on the infrastructure network as a whole, and they can often be attributed to issues during construction or



localised problems. Defects are also more commonly picked up after the occurrence of a more significant event, and therefore limited in knowledge in precursory features which could be potentially monitored.



2. WP2 Analysis of Performance of SGMs and General Earthworks

2.1 Introduction

As part of Task 1-456, Work Package 2 aims to understand and set out how the performance of all geotechnical assets will be assessed, measured and benchmarked. For this Work Package, it has been agreed that condition is analogous to performance and will be applied to individual earthwork assets. The consequential impact of changes in asset condition on the availability or performance of the network will not be assessed. This Interim Summary Report aims to summarise the work completed and progress to date with this Work Package and will set out recommendations for consideration and incorporation in other Work Packages.

There have been a number of tasks for Highways England which categorise and report potential performance, based on condition. In order to ensure consistency and to gain most benefit from previous investment, this task has principally reviewed ongoing and previous tasks outputs and assessed the application of processes and terminology for suitability in achieving the objectives of this Work Package.

A review of how performance is approached by Highways England drainage asset teams introduced the concept of Condition and Serviceability of assets. Consideration of ongoing Task 1-266 Condition Indicators is also important to ensure aligned approach to condition reporting. A review of the current Geotechnical Slope Hazard Ratings has also been carried out to ensure that there is no duplication, but consistency between existing ways in which condition and performance are commonly reported by Highways England.

Most critically for the completion of Work Package 2, it has been identified during the progression of the sub- tasks that the anticipated performance of the assets is required to feed into, and provide, the comparative baseline for assessing current condition and performance.

2.2 Existing information

Aims and objectives The aim of this Work Package was to define a framework to report the current performance and condition of general earthworks whilst considering approaches which are used by other asset owners and tasks which have been developed within Highways England. This Work Package focused on the development and links with other completed and ongoing tasks, and a reference to serviceability has been explored to allow better consistency and remove subjectivity when assigning and understanding asset condition.

The methodologies and processes applied in the below tasks have been examined by their applicability to report performance at asset level.

The existing tasks which have been reviewed are:

• Geotechnical Condition Indicators (Task 1-266);

• Slope Hazard Rating (Task 197, 2014 & 2017).

Consideration of the Drainage Standard CD 535 structural and service grade definitions has also been given to promote consistency across assets. Data Sources It has been proposed and agreed that at this time, performance of the general earthworks is analogous to condition, therefore the information required to determine performance will be captured, held and readily available within GAD.



An extract from HAGDMS received on 14 May 2018, comprised a full extract of the historic and current GAD data. This extract included all earthwork and observation data which has been used in assessing the application of the tested approaches, to determine performance and condition. Limitations

• Data accuracy – The main limitations of this task are associated with the quality and consistency of the GAD data available, as the GAD data inherently contains human error and some subjectivity of classification of defects.

• Observation overlaps – Many of the existing task approaches use the uniquely recorded observations to be report lengths of asset/network condition. However, in some cases, observations on an asset may overlap resulting in an overestimated total length of classified asset/network. This is seen in Task 1-266 and Task 197, where in some cases, there is greater than 100% of the earthwork length reported and included in subsequent reporting.

• Data relationships – from a critical assessment of the data available for the geotechnical assets, it is evident that the current data structure does not permit the direct correlation between the presence of a defect and its impact on the performance of an SGM or conventional geotechnical asset at that location. An investigation has been conducted into use of the existing data to establish these relationships, and in Work Package 1, a manual review of defect observations coincident with recorded SGMs was undertaken to identify and report the potential condition of SGMs. However, until adjustments are made to the GAD data structure, it is not possible to conclusively relate defects to individual SGMs.

2.3 Other asset owners

A critical review of the approaches taken by other geotechnical asset owners has been completed to establish how condition and performance of assets is defined and recorded. Details of how this is conducted by the major geotechnical asset owners is outlined in the respective sections below using information which is readily available from public sources and engineering standards.

Network Rail The approach taken by Network Rail includes inspections of five chain lengths (~110m) of earthworks to examine the likelihood of failure of an asset. These inspections aim to qualitatively assess the earthworks ability to perform its function. The process applies to all the earthworks within the boundaries of Network Rail infrastructure, including approach embankments, approach cuttings, tunnel cuttings, slopes above tunnel portals, nailed or reinforced structures and any embankments that also act as a coastal, estuarine or river defence. The process also covers third party slopes which may impact the infrastructure and are examined following three category approaches. These include Rock Slope Hazard Index (RSHI), Soil Embankment Hazard Index (SEHI) and Soil Cutting Hazard Index (SCHI). Details of these can be found in NR/L3/CIV/065 Module 1-3. Earthwork examinations are standardised to identify and record any signs of instability and are carried out in accordance with NR/L3/CIV/065. The examination process records the degradation (or improvement following remediation) of earthworks to enable decisions to be made on how to control risks. The likelihood of an earthwork to fail is derived from its hazard index as outlined above. The index is generated using an algorithm which is based mainly on surface observations and visible features collected during an examination. Other information which is input into the data include interventions, geology and national data. The software used to generate the hazard index is known as Civils Strategic Asset Management Solutions (CSAMS) examination software. Each of the earthworks will be assigned to a hazard category from A-E based on the result of the algorithm and this category determines the cyclical examination interval, similar to the repeat inspection period used by Highways England. The full list of information required to be collected during inspections is included in the above referenced documents. Environment Agency To achieve effective management of their assets, the Environment Agency require reliable predictive tools and methodologies to use as aids in the estimation of asset life under different conditions of environmental



exposure and maintenance schedules. To support this, the Environment Agency approach to asset condition is based on the use and application of condition grade deterioration curves. These are a series of models which are applicable to different types of flood and coastal defence assets and are suitable for estimation of future condition taking in to account characteristics related to the environment, asset age, material types, construction and maintenance. Embankments for reservoirs are also inspected under the Reservoirs Act 1975 which is legislation to make provision against escape of water from large reservoirs, lakes or lochs. Condition grades are based on definitions given in the EA Condition Assessment Manual (2006) and are graded 1-5 and described as Very Good-Very Poor respectively. The condition and deterioration of an asset is categorised based on the AIMS asset classification as well as the environment in which the asset is located and the classification of the type of asset. The asset inspections and assessments undertaken by the EA are not specifically completed by a geotechnical engineer, although inspectors are required to complete basic training to reach the required competencies Welsh Government The Welsh Government establish the condition of their earthworks on the trunk and motorway network (SRN) by following the procedure set out in HD41/15, Maintenance of Highways Geotechnical Assets. Transport Scotland The approach adopted by Transport Scotland for their SRN is similar to that implemented by Highways England, however it is believed that the approach tends to be completed on a reactive rather than proactive basis. Walked visual condition inspections take place to establish the condition of ancillary assets, although there is no complete geotechnical asset database which has been compiled. Geotechnical assets are grouped as ancillary assets along with traffic signs and signals, drainage assets, road lighting, fences and barriers, technology assets and landscaping. Guidance on the procedure for condition surveys is provided in Transport Scotland’s Trunk Road Condition Manual which details the procedure for rating the condition of assets according to five levels of condition categories. These are Excellent, Good, Average, Poor and Very Poor. This information informs maintenance strategies and assesses how an asset is performing over time. London Underground (LUL) LUL follow Standard S1054 Civil Engineering Earth Structures as their approach to manage whole life cycle requirements for Earth Structure assets where embankments and cuttings are classified as equal or greater than 1m height. S1054 utilises a condition rating tool where a rating is produced for each transect (100m) or feature, as a measure of the state of the slope based on extents, severity, condition and priority for the entire earth structure. Geology is assumed using inbuilt engineering parameters. The condition rating is produced as a percentage where 100% would be considered as peak condition in comparison to how the condition the earth structure would be anticipated. The rating then defines the earth structure as condition poor, marginal, serviceable or good and the subsequent frequency for repeat inspection.

2.4 Task 1-266 condition indicators

The condition indicators generated in Task 1-266 are used as an enhanced geotechnical asset condition metric, which is a supporting metric to the overall KPI that is reported to the ORR monthly. The condition grade generated provides a snapshot of the condition of the network asset at the time of the last inspection. This task reports length of network assigned a condition grade (based on correlation of classified geotechnical observations which have been recorded according to HD41/15 Class and Location Index) as a percentage of network length. The network length in this case has been defined as one HAPMS centreline length. The matrix for converting the HD41/15 classification into condition grade can be seen in Table 2-1. It should be noted that this methodology assesses the ratio of ‘poor’ asset to route length based on an equivalent centreline of the network.



Table 2-1: Task 1-266 Condition Indicators related to Class and Location Index

Location Index Class

1A 1D

A Very poor Very Poor

B Very poor Poor

C Poor Fair

D Fair Fair

Based on this methodology, a condition grade can be derived for lengths of earthwork that have a classified observation within their extents. The task defines five overall categories of condition grade as summarised in Table 2-1, with length of assets with no classification graded as “very good” and anything other than Class 1 graded as “good”. This would include Class 2 “at risk” features and those which have been repaired (Class 3).

Table 2-2: Description of each Condition Grade from Task 1-266

Condition Grade Description

Good Very Good No structural degradation and no detrimental characteristics present

Good Characteristics that can be detrimental to the asset structural integrity are present, but not degradation has occurred

Fair Asset structural integrity is degraded / compromised but with no effective reduction to assets ability to perform its function

Poor Poor Asset structural integrity is compromised but remains a reduced / degraded ability to perform its function

Very Poor Asset ability to perform its function is compromised

The application of this methodology and definitions has been assessed to understand the potential application to report condition along the length of each asset, as opposed to reporting against total network length.

As part of this task the above was reviewed and potential adaptation proposed for it to be applied and reported at earthwork asset level. It is suggested that where an observation is recorded as a Class 2 according to HD41/15, it is assigned a ‘Good’ condition grade. Where there is a Class 3, it is assigned as ‘Very Good’. It is assumed that if there are no observations recorded on an earthwork, the earthwork and observations can be categorised as being in ‘Very Good’ condition. This would align well with the grade definitions set out in Task 1-266 with the slight variation that lengths of assets that have been repaired and are classified as Class 3 are assumed to be in Very Good condition with lengths with no features. This would also tie in with the classification in accordance with HD41/15 that these are of a lower feature grade than Class 2 features.

By applying the adapted methodology, the total length of condition category for each earthwork can be calculated and reviewed. An extract of how the output can be reviewed is included below as Table 2-3.



Table 2-3: Pivot table extract of condition grade length for each earthwork.

EW ID Length (m)

V.Good Good Fair Poor V.Poor

44 125 1

46 158 243

50 56

51 125 447 20

56 101 1

61 432 75 59 18

64 106 10

65 147 68

67 225 17

69 247

The total length of classified observations on the network is 821 km. This represents approximately 6% of the total network geotechnical asset length. The total lengths and count of the number of observations recorded for each category are included in Table 2-4 and represented visually in Figure 2-1. Note: Very Good below is representative of the Class 3 observations on the network for comparative purposes.

Table 2-4: Count and length of observations assigned a condition grade

V.Good Good Fair Poor V.Poor

Count of observations

863 2993 4660 1019 257

Length (km) 75.8 410.7 271.5 47.4 15.9

% of total EW length

0.55 3.02 2.00 0.35 0.12

Based on the results above, approximately 16 km of the total earthwork length is considered to be in Very Poor condition. The results identify that around 410 km of the network fall into the Good condition category, which translates from HD41/15 classifications of Class 2 observations which are considered to be “at risk”.

Figure 2-1: The length of classified observations within each condition grade

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271.484

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Condition Grade

Observation Condition Length



This information is useful to understand the overall length and possible percentage length of condition grade at a strategic level. However, this may not be particularly helpful when reviewing and reporting comparatively at specific asset level.

Application of a worst-case

The potential to report a singular condition grade for each asset was examined, taking the worst case condition grade and associating it to the whole asset, summarised in Table 2-5. The main limitation with this approach is that it would overestimate the asset length of poorer condition. It would however remove the limitation of overlapping observations. The count of Very Good condition earthworks also includes the number of earthworks which have no defects recorded and therefore have been assigned to this condition category.

Table 2-5: Summary of earthworks condition based on recorded worst case condition of observation

V.Good Good Fair Poor V.Poor Grand Total

Number of assets 41691 2184 4211 971 257 49314

Length of asset (km) 10856.7 657.0 1597.0 385.0 108.9 13605.0

% of number of EW’s 84.5 4.4 8.5 2.0 0.5 100.0

% of network asset length

79.8 4.8 11.7 2.8 0.8 100.0

Comparative to Task 1-266 Reporting A comparison of the ratios of conditions to asset length has been tabulated following the approach identified by the Task 1-266 work. The initial condition indicators task generated the ratio based on HAPMS route length using a single centerline with a length of 5842km and didn’t include DBFO areas. The approach here uses the total earthwork asset length of 13,605km and included DBFO areas. The previous task also only reports the “poor” condition assets whereas the individual condition categories have been recorded here. The results of this can be seen in Tables 8 and 9. The ratios are based on the length of recorded condition against the total earthwork asset lengths.

Table 2-6 – Task 1-266 Ratio result.

Length of “poor"* asset Ratio of “poor” asset to route length**

53.85 km 9 m per km

*Poor asset as defined by Task 1-266 are assets which fall into the Poor and Very Poor condition grade as shown in Table 2-2.

**HAPMS route length = 5842km. The route length is the equivalent of network carriageway centreline. This would by comparison

equate to approximately half the length of the network geotechnical asset length.

Table 2-7: Ratio of individual observation condition to total asset length.

Length of asset Ratio of “condition” to asset length

V.Poor 15.9 km 1.2 m per km

Poor 47.4 km 3.5 m per km

Fair 271.5 km 20 m per km

Good 410.7 km 30.2 m per km

V.Good 12859.5 km 945 m per km



It is evident from these results in Table 2-7 that 4.7m per km of the earthwork asset length falls into the “V.Poor” and “Poor” condition categories compared with the recorded 9m per km in Task 1-266. A reason for this is in part that Task 1-266 creates a ratio based on the HAPMS centreline length and not the total earthwork asset length. Earthworks will generally be present on the left and right side of the carriageway and therefore a HAPMS centreline does not represent both sides of the carriageway. By doubling the length of the HAPMS value to account for both carriageways, the ratio is more consistent across both approaches.

945 m per km or approximately 95% of the earthwork assets has been classified as “V.Good” highlighting that a large proportion of the network is recorded to be in Very Good condition following this approach.

Table 2-8: Ratio of worst-case condition to asset length

Length of asset Ratio of “condition” to asset length

V.Poor 108.9 km 8 m per km

Poor 385.0 km 28 m per km

Fair 1597.0 km 117 m per km

Good 657.0 km 48 m per km

V.Good 10856.7 km 798 m per km

When considering the worst case recorded condition approach results in Table 2-8, a significant length of asset (around 2000km) becomes a higher classification. Comparably one of the largest differences is that the length of asset categorised into the “V.Poor” category is approximately 7 times larger which significantly overestimates the length in the worst-case condition. The same can be said for the “Poor” condition category which has a recorded length of 8 times greater than that following the observation condition approach. This result largely mis-represents the condition of the earthwork assets on the network and is not therefore recommended to be taken forward or adopted at a network wide reporting level.

Conclusion

The nature of individual observations associated with an earthwork require some interpretation in order to develop an understanding of the general condition of the earthwork at asset level. Aspects such as the number and nature of observations, extent of defects, defect history, and understanding whether the defects are due to isolated or inherent causes require consideration. It is therefore recommended that a method of calculating and reporting condition of each of the assets, based on these factors, is derived and applied. This would also need to consider the anticipated condition based on its engineering characteristics for comparison and ultimate assessment of performance.

Assuming that the condition of the asset is represented by the worst-case recorded observation, is likely to overestimate and mis-represent the condition of the earthwork asset significantly. As highlighted above, this approach can overestimate the length in the condition category by up to 8 times that of what is recorded on the network. It is therefore recommended that this approach is not adopted.

To utilise and provide consistency with the observation condition approach, it was suggested that a score-based approach could be adopted and applied to each earthwork, based on the length of the asset recorded in each condition category. This would provide an overall condition for each of the individual earthwork assets, which can then be bounded by set criteria. This outcome has led onto research into the derivation of the Slope Hazard Rating to see if this could be adapted and applied to define an asset condition grading.



2.5 Slope hazard rating

Reviewing the outputs from applying an adapted Task 1-266 methodology, it is noted that reporting the

length of classified observations alone does not help to understand the condition of individual assets, and

reporting the worst case will disproportionately misrepresent condition, and the length of the network in

each condition grade. The potential to develop and derive a score per asset, based on the proportion of

asset in each recorded condition was hypothesised. This in turn led to a review of the work completed

previously by Task 197 in 2014 and updated in 2017, as part of the development of the network wide Slope

Hazard Rating.

The Slope Hazard Rating considers the total length of classified observations on assets constructed using

cohorts of geologies, grouped by the relationship between the maximum height and associated angle of

the slopes. The lengths of observations are weighted based on the classification of observation (in

accordance with HD41/15) and expressed as a percentage length of assets within the same cohort. See

Figure 2-2. The overall hazard rating from very low to very high has then been defined based on achieving

a doubling of asset counts between rating bands, with the lower bands having a greater number of assets.

Figure 2-2: Slope Hazard Rating (Hazard assessment note: 2017)



Conclusion

In the calculation of the length of asset which is defective for each cohort, only classified observations

which relate to slope stability features are included, this is to better represent lengths of earthworks

which are unstable due to their engineering characteristics, rather than affected by other influences. The

rating generated is also applied to all assets within the cohort, regardless of the number or length of

classified observations on the individual assets. This will result in assets which have no recorded

features to be classed as very high hazard rating.

In its current form the Slope Hazard Rating does not consider the influence of SGMs in the morphology

input. This would overlook instances where slopes have been constructed using SGMs such as geogrid

and other reinforcement to allow them to be stable at greater angles.

Slope hazard rating was developed to provide an analysis of potential slope hazards based on the

recorded performance of cohorts of assets. This generalised cohorts and considers slope geometries, to

give a high-level overarching rating to each of the assets. It is not intended to be used as a standalone

representation of the risk or condition of existing in-situ assets.



3. WP3 Deterioration Models for SGMs and General Earthworks

3.1 Introduction

The aim of Task 1-456 is to improve understanding of both the current and future condition and performance of geotechnical assets.

Work Package 3 (Development of Deterioration Models for SGMs and General Earthworks) focusses on the future condition aspects of Task 1-456.

This task has been undertaken with an aim of establishing a robust means of addressing questions from the regulator regarding future performance, whilst considering the challenging variables associated with ‘predicting the future’.

Work undertaken as part of Task 1-456 and other Resilience Programme tasks (for instance Task 1-906) has identified SGMs that are most prevalent on the SRN, those that are most often co-located with defects, and those that are most often associated with verified defects. Questionnaire surveys have been implemented to sample the experience of the wider Highways England Geotechnical Community and other relevant UK geotechnical asset owners.

WP3 work has been divided into two separate sub-tasks (‘approaches’) which reflect two different ways of investigating the future performance of earthworks assets, as follows:

• Probabilistic Modelling of Earthwork Deterioration (‘Bottom Up Approach’)

• Using geotechnical data to make a quantitative assessment of earthworks performance.

• Analysis of Earthwork Principal Inspection Observations (‘Top Down Approach’)

• Evaluating existing predictions of performance.

Following initial work on the ‘Bottom Up’ approach in accordance with the original project scope, the additional ‘Top Down’ method utilising existing earthworks inspections data was also pursued. This second method was introduced to analyse the robustness of the current, most widely applied means of predicting earthwork deterioration (principal earthwork inspections). Readily accessible data of predicted feature grade is available through HAGDMS for this assessment.

The outputs of this Work Package will:

• Support the identification of high-risk earthworks assets within the network.

• Outline a proof of concept for a repeatable, quantitative approach of assessing risk.

• Improve understanding of reliability of current predictions of deterioration.

• Improve ability to forecast the future performance of earthworks on the SRN.

3.2 Probabilistic Modelling of Earthwork Deterioration (‘Bottom Up’ Approach)

Aims and objectives

• The aim of this approach is to produce a quantitative estimate of deterioration using probabilistic analyses.

• This probability is obtained from probabilistic analysis using modelling software (Slope/W). This uses assigned parameter distributions, based on geotechnical data from a specific geology type. The analysis also considers earthwork geometry and age and differentiates between embankments and cuttings.

• The objective of this work is to investigate whether a probabilistic analysis can be used to determine the susceptibility of earthwork assets to deterioration, and occurrence of defects. Defects in the context of this work are considered to be anything which would warrant remediation works and associated cost expenditure.



• This approach aims to develop a proof of concept for a repeatable approach which can be adapted and applied to multiple geology types, and eventually the entire SRN.

Background

Slope stability analysis methods have traditionally used limit equilibrium software such as GEO-SLOPE GeoStudio Slope/W. This analyses earthworks slopes to obtain a single critical failure plane with an associated factor of safety. This finds the lowest factor of safety for a single set of defined material parameters which remain constant (deterministic analysis).

It has been postulated whether this ‘deterministic’ method offers a realistic assessment of slope stability, given the natural variability of parameters and the practical difficulty in obtaining measurements of relevant material properties.

For this task, a probabilistic modelling method is considered as an approach for determining deterioration of earthworks. This approach is based upon analysis using a Monte-Carlo Simulation which repeatedly randomly samples input parameters based on a statistical distribution of those parameters.

A probabilistic approach allows calculation of a theoretical probability of failure based on the variation of geotechnical parameters. This analysis uses existing geotechnical parameter data available from projects. This data has been obtained in AGS format and is stored within the database software HoleBASE SI / Keynetix Cloud. The extracted AGS data has been processed to calculate probability density functions for input into Slope/W.

The probabilistic analysis (using Slope/W) provides a factor of safety value and a probability of failure for a range of slope geometries adopted, such as varying heights and slope angles.

As an initial proof of concept, this methodology has been tested by using geotechnical parameter data obtained from naturally occurring, in-situ London Clay Formation. The London Clay Formation has been chosen due to the large parameter data set readily available, and the high level of previous experience of the project team of working with this strata.

The purpose of considering this methodology is to assess whether it can be applied in a practical manner to the network, i.e. it can be used by the wider industry in GDMS, and whether is suitable to be adapted and applied to other geology types as part of future tasks.

Data requirements

For this methodology, data is required for the following geotechnical parameters (which are needed directly or indirectly to derive parameters):

• Bulk Unit Weight (kN/m3)

• Plasticity Index (%)

• Angle of shearing resistance, φ’ (degrees)

• Cohesion, c’ (kPa)

• Pore water pressure, which is represented using the coefficient, ru

Data for this analysis has been obtained from sources where appropriate quality control checks have been carried out on the AGS files and by applying engineering judgement.

It is necessary to ensure that there is an appropriate spatial distribution of projects, in order to pick up regional geological, stratigraphic and geotechnical variation.

Sources of data

AGS data from Atkins projects held within HoleBASE SI for ground investigations within the London Clay Formation has been used as the primary source for this task.

A review of AGS data stored on the Highways England Geotechnical Data Management System (GDMS) was also undertaken to assess the potential for data extraction. Preliminary analysis of the data concluded that the AGS reports in GDMS currently lack the quantity and quality of data required for this task; therefore, this data has not been utilised.



Values of phi (ɸ’cv), and cohesion (c’) can be directly taken from laboratory test results, for example from triaxial tests. However, these results are often highly variable as a result of varying sample sizes and differing methods of reported interpretation. Additionally, cohesion values obtained from laboratory testing is often unreliable due to the rate of shearing and the stress range tested. Cohesion and phi (ɸ’cv), are also dependant on stress, typically represented by a curved failure envelope such that at low stresses c’ = 0, with higher angles phi (ɸ’cv), value. There is also a relatively small number of tests per project when compared to classification tests for instance. Accordingly, the use of laboratory shear test results is unlikely to provide a reliable distribution of data based on the current datasets.

Considering the above when obtaining values of cohesion (c’), it is acknowledged that engineering judgement is required to assign suitable values for this analysis. Similarly, for pore water pressure coefficient (ru) a lack of readily available and reliable measurements from ground investigations means that engineering judgement has also been used to assign these values.

An alternative approach to assess shear strength parameters is through correlation with classification tests such as plasticity index to obtain a critical state or constant volume phi (ɸ’cv) value. This will provide a larger and potentially more consistent data set of phi (ɸ’cv) values than obtained from the triaxial test data. Plasticity index has therefore been used to derive geotechnical parameters for this task.

Example suitable projects to extract data from for this task were identified as:

• Site investigation for large highways schemes (e.g. dualling/widening, Smart Motorways, new build highways); especially where there is scope to compare data from historical site investigation to more recent investigation;

• Projects with extensive geotechnical laboratory testing data, particularly from tests giving the required parameter information as direct results;

• Non-highways site investigation data for large linear schemes (e.g. rail, tunnelling) for supplementary parameter information. This would provide additional data for regional context (i.e. for the whole London Clay Formation).

At this proof of concept stage, the data is primarily extracted from Atkins projects which have involved site investigations being carried out on the M25 – for example M25 Later Upgraded Sections (LUS) schemes.

Definition of frequency distributions

Slope/W analyses require a range and a frequency distribution of each geotechnical parameter. This enables the software to consider the potential variability of the geology of earthworks.

For phi (ɸ’cv), a normal and log normal distribution have been defined. From this, the ‘best-fit’ distribution was selected using statistical testing. For cohesion (c’) and for pore water pressure coefficient, ru, a triangular distribution has been defined. For Bulk Unit Weight (kN/m3), when plotted the distribution showed very little spread. As a simplification, the mean value is used in all analyses.

The characteristics of the distributions i.e. the parameters of the populations, which may include mode, median, mean, mean offset, minimum, maximum and standard deviation of each geotechnical parameter have been assessed to aid the definition of the distributions.

The frequency distributions of the in-situ material produced for each geotechnical parameter is applied to both embankments and cuttings with the exception of ru; where separate distributions have been identified to represent differences in the potential pore water condition in embankments and cuttings.

Probabilistic analysis

The slope geometries to be analysed will comprise 4 No. slope heights and 6 No. slope angles which have been chosen to be representative of the Highways England network. These have been identified as part of WP3 to be the most commonly occurring slope angles on the network.

These combinations produce 24 No. geometry models. The slope geometry combinations to be analysed are provided in Table 3-1:



Table 3-1 - Slope geometry combinations to be analysed

1:2 1:2.25 1:2.5 1:2.75 1:3 1:4

2.5m

Φ’, c’, ru to be varied in turn

5.0m

10m

15m

Sensitivity of each parameter will be considered by analysing the probability of slope failure using a Monte Carlo simulation of each input parameter distribution in turn. The analysis adopts the median value for the other parameters that remain constant.

Therefore, there will be 96 No. iterations of analysis needed to assess each geotechnical parameter.

A further 24 No. iterations will be undertaken where a Monte Carlo analysis will be applied to the four geotechnical parameters simultaneously.

Only significant slope failures that would result in the need for cost-effective mitigation measures being undertaken are relevant to this task. This will be applied by assigning the entry and exit points of slip surfaces in Slope/W at the toe and crest of slopes. The minimum slip depths were set to be 0.2 x slope height.

The aim of the analyses as a whole is to establish the ‘potential for deterioration’, based on the existing representative geotechnical parameters for a geology type at ‘Formation level’. This is primarily defined by the probability of failure, based on probabilistic Slope/W analysis.

This will then be developed further on an individual “Site specific level’ based on site specific parameter information with the potential to further consider the age and present-day condition context (from principal inspections) of existing earthworks as part of a future task.

Parameter Derivations

Bulk Unit Weight (kN/m3)

For all analyses a single bulk unit weight of 20kN/m3 has been applied. This value was selected as the data showed very little variation, meaning using a fixed value would not compromise the results of the analysis whilst greatly reducing the complexity and numerical effort required.

Pore water pressure coefficient (ru)

Due to the challenges of measuring the parameter ru, the distributions were specified using engineering judgement.

Two ru distributions were specified, to reflect the different groundwater regimes typically assumed to be present in embankments and cuttings (i.e. typically lower groundwater in an embankment than in a cutting, see Figure 3-1). The distributions are specified in Table 3-2 below:

Table 3-2 - Pore water pressure coefficient (ru) values derived for Slope/W analysis

ru Minimum Value Maximum Value Mode Value

Probability Value Probability Value Probability Value

Cuttings 0.1 0 0.2 0.5 0.4 0.3

Embankments 0.2 0 0.1 0.5 0.4 0.15



Figure 3-1 - Pore water pressure coefficient (ru) distributions

Cohesion, c’ (kPa)

Similarly to ru, it is acknowledged that there is a degree of subjectivity when deriving the parameter c’ from geotechnical data.

Due to the variation in interpretation of c’ from one direct testing result to another, and a lack of consistent results in the data set from sources such as triaxial testing, it was decided to define the distribution for c’ from engineering judgement.

The proposed distributions for this task based on engineering judgement and taking account that the

London Clay Formation is a high plasticity, overconsolidated soil are specified in Table 3-3 and Figure

3-2 below:

Table 3-3 - Cohesion (c') values derived for Slope/W analysis

Cohesion (kPa)

Minimum Value Maximum Value Mode Value

Probability Value Probability Value Probability Value

All Earthworks 0.1 0 0 5 0.4 2.5

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0 0.1 0.2 0.3 0.4 0.5 0.6

Pro

bab

ility

Ru

Pore water pressure coefficient distribution

Embankments Cutting



Figure 3-2 - Cohesion, c’ (kPa) distribution

Angle of shearing resistance (phi), ɸ’cv, (degrees) Phi values were derived from available plasticity index tests. This section describes the methodology adopted for this process. Ground investigation data from 7 No. projects anticipated to include testing data from the London Clay Formation data (6 No. M25 schemes and Thames Tideway East) was extracted from Atkins HoleBASE SI / Keynetix Cloud. This software is a geotechnical data management platform where AGS data from projects is stored. The data was collated into a database of approximately 32,000 records and then queried based on the following criteria:

• Only samples identified as London Clay Formation in GEOL code were retained;

• Any entries without location data were removed, as geology type could not be verified;

• The location of samples was compared against British Geological Survey 1:625,000 Bedrock

Geology maps, and samples located within areas not mapped as London Clay Formation were

removed;

• Entries sampled from below 20 mBGL were removed as they were not considered representative

for embankments or cuttings on the SRN;

• Blank and zero value entries were removed;

Following the relevance filter a ‘cleaned’ dataset of approximately 500 entries considered to be representative from four different projects remained, approximately 1.5% of the original dataset. Values of phi (ɸ’cv), were estimated using industry standard correlations with Plasticity Index. Reviewing the background for each of the correlations showed that Lambe and Whitman’s correlation (Proceedings of the 3rd International Conference on Soil Mechanics and Foundation Engineering (Zurich), v. 1:126; T. C.) provided the best estimate for use in a probabilistic analysis. Other correlations (e.g. BS8002) were discounted as these tend to provide ‘design’ values of phi (ɸ’cv), which will be less representative for a probabilistic analysis. Phi (ɸ’cv), values were then estimated using the Plasticity Index by applying Lambe and Whitman’s equation for normally consolidated soils:

𝜙′ = −6.686 ln(𝑃𝐼) + 50.92

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0 1 2 3 4 5 6

Pro

bab

ility

Cohesion, c' (kPa)

All Earthworks



It should be noted that Lambe and Whitman’s correlation represents an average value, using a range of soils and that there is potential variation for higher and lower values for any given PI.

It is also noted that the tests were carried out on re-moulded soils, which would be representative of material which has been artificially emplaced in an earthwork. However, the derived values considered in this case to represent the phi (ɸ’cv), are for overconsolidated soils such as the London Clay Formation and therefore are an approximation of the conditions that would be present in an earthwork.

From the ‘global’ merged dataset which incorporates all data, a global data set for London Clay Formation (‘formation level’) and four ‘site specific’ datasets relating to the four remaining source projects were produced for analysis.

An empirical rule statistical approach was adopted to enable the automated removal of anomalous values from the datasets. The standard deviation and mean were calculated for each of the datasets (one global and four site-specific) and any data points outside of two standard deviations either side of the mean were removed. This method retained approximately 95% of the data.

In order to select the best-fit distribution for the datasets, statistical tests were used, with the distribution with the highest score selected for the analysis. At this stage, each of the selected distributions was visually confirmed; however, going forward this step could be skipped.

The resulting best fit distribution type and the mean, standard deviation, maximum and minimum values of that distribution were then output as parameters for use within the Slope/W analysis. Table 3-4 presents the results used within the Slope/W analysis and Figure 3-4 presents the global data distribution. Table 3-6 summarises the input data used for the Global Distribution Analysis.

Table 3-4 - Phi (ɸ’cv), (degrees) values derived for Slope/W analysis

Φ’ Global

Site Specific

M25 DBFO Section 1 J16-23

Thames Tideway East

M25 LUS M25 Rapid Widening

Section 4

Minimum (°) 22.7 22.4 23.7 25.8 24.8

Maximum (°) 29.1 29.1 28.4 29.7 28.0

Mean (°) 25.8 25.5 25.6 27.7 26.3

Standard Deviation (°)

1.26

1.21 1.04 1.13 0.804

Offset * 0.031 0.029 0.021 0.023 0.012

Distribution Type Log

Normal

Log Normal Log Normal Log

Normal Log Normal

* Skew represents the difference between the mean value of the normal and log normal distributions

Table 3-5 – Ground Model Summary (Global Distribution)

Parameter (units)

Min

imu

m

Min

imu

m

Pro

bab

ility

Maxim

um

Maxim

um

Pro

bab

ility

Mean

/Mo

de

Mean

/Mo

de

P

rob

ab

ility

Sta

nd

ard

D

evia

tion

Offs

et

Dis

tribu

tion

Typ

e

Φ’ cv (°) 22.7 - 29.1 - 25.8 - 1.26 0.031 Log Normal

c’ (kPa) 0 0.1 5 0 2.5 0.4 - - Triangular

γ (kN/m3) - - - - 20 - - - Uniform

Ru (-) – Cuttings 0 0.1 0.5 0.2 0.3 0.4 - - Triangular

Ru (-) – Embankments 0 0.2 0.5 0.1 0.15 0.4 - - Triangular



Figure 3-3 - Phi (ɸ’cv), (degrees) distribution

‘Formation level’ analysis

This analysis considers all parameter information collated for material classified as London Clay Formation. It incorporates a population of data representing the geospatial distribution, including regional and stratigraphic variation of the geological unit. This data is considered to provide ‘typical’ parameters for the geological formation – i.e., a ‘Formation level’ assessment.

‘Site specific level’ analysis

This analysis focusses on ‘Site specific level’ London Clay Formation parameter information.

Analysis of these site-specific parameters considers the statistical distributions obtained from local investigations and aids comparison with the formation level dataset.

Comparison between the formation level and site-specific level parameters would enable the user to highlight areas where the vulnerability of earthworks is greater than would typically be expected (compared to the ‘Formation level’ analysis), the site could be investigated in greater detail to assess further.

Results

Each of the distributions has been input into Slope/W, and programmatically iterated through each of the combinations set out in the methodology. The results from all of the analyses were then processed into a matrix presenting percentage chance of failure for each slope height and angle assessed.

This matrix is included as Appendix 1. Within Appendix 1, global, formation wide London Clay Formation parameters are analysed first, before analysis on a site specific level is undertaken.

The matrix was then mapped to relevant assets. Relevant asset data is defined as that falling within Highways England Area 5 and having a London Clay Formation calculated geology code. The critical slope angle in the earthwork and the corresponding height were used to lookup, interpolating as and when required. This enabled the results of the analysis to be viewed geospatially within a GIS software.

A visual comparison between the method, known defects in GAD and the existing Slope Hazard Index Rating (2017) has been carried out. This comparison shows a good correlation between locations where



defects have occurred or have been identified to be at risk. Some example locations of this can be seen in Appendix 2 with accompanying annotations.

‘Bottom Up’ Conclusions & Recommendations

The proof of concept for the bottom-up approach has identified a number of limitations and possible restrictions to this approach. Notably key parameters that impact the assessment of slope stability using limit equilibrium analysis are either limited in quantity or are affected by factors influencing the reliability of the dataset:

• Effective shear strength parameter (phi’peak) – there is likely to be variable degrees of sample disturbance due to sampling methodology (e.g. use of U100, UT100, rotary core samples), diameter of test sample (e.g. 38mm or 100mm), confining pressures for the tests and interpretation of results (e.g. based on single sample, set of 3 samples, or using all samples form a single site).

• Effective shear strength parameter (c’peak) – similar to above, coupled with known difficulties/accuracy of comparing laboratory established values for cohesion when compared to back analysed failures.

• Porewater pressure coefficient (ru): no reliable data

As a consequence of the above the distributions of both c’ and ru parameters have been assessed based on engineering judgement rather than using any site-based laboratory test or groundwater monitoring results. Estimates of effective shear strength parameter (ɸ’cv) have instead been derived from plasticity data, which have the advantage that a large dataset is available but that the empirical correlation used is a general one and may or may not be precisely correlated to the stratigraphy being considered.

The laboratory testing captures the soil parameters at the time of testing, these do not necessarily reflect the current age of the earthwork nor the parameter at the time of construction. However, of the parameters that could be measured reliably (i.e. plasticity index and bulk density) these would not be expected to change over the life of the earthwork based on our current knowledge

Literature suggests that within earthworks in over-consolidated soils a ‘softening’ occurs with the tendency for the effective shear strength parameters (c’ and phi’) to reduce from a peak value towards the critical state (or constant volume) value; the magnitude of in reduction is dependent on the plasticity of the soil. Similarly, in cuttings there is evidence that porewater pressures equilibrate and recover over time.

However, the rates of these changes are less well documented, nor the depths of potential ‘softening’ known. Softening may be more influential in the lower height slopes if for example softening only takes place in the upper 2m.

Other time effects that are difficult to quantify in the current probabilistic approach include climate change resulting in increased or porewater pressures or simply that the older an earthwork is the more likely it is to have been exposed to higher porewater pressures which in turn could influence another time related factor such as progressive failure.

Other time related factors not included in the modelling are the potential for deterioration of other features due to poor maintenance such as drainage, or changes in the geometry of the slope due to subsidiary construction or maintenance activities.

The need to use engineering judgement and empirical relationships to define the geotechnical parameters has perhaps resulted in the selection of parameter values that represent the longer-term conditions that might be expected to occur in the slope (ɸ’cv rather than ɸ’peak and ru for example). Consequently, the probabilistic approach perhaps identifies the cuttings and embankments that have the greatest risk of failure in the long term and potentially provides a quantification of this. However, further work would be needed to benchmark actual failures with a theoretical probability of failure and how this may also be influenced by aging of the earthwork.

Despite the above comments the approach of ‘global parameter’ probability could potentially highlight where failures are most likely, and the approach could also be used to compare other geological formations to quantify if there were formations (and therefore length of embankment/ cuttings more likely to be susceptible to failure across the network).



However, whether the ‘proof of concept’ and probabilistic approach of this study (considering the London Clay Formation) provided a more reliable approach than just considering a hazard rating based on slope height and slope angle alone has not been investigated at this point. It is possible that probabilistic method could aid refinement and quantification of a slope height and slope angle hazard rating for different geological formation, but this would require further study.

For the London Clay Formation there was not significant variation in the geotechnical parameters available from the database (i.e. bulk density and plasticity index) between specific sites when compared to the global population. Consequently, within the accuracy of the approach it is difficult to reliably indicate whether the application of site-specific variance improves the modelling significantly or not. However, the site-specific probabilistic approach may provide benefits when considering strata which exhibit a greater variation in properties either due to lithological variation within vertical stratigraphy of the formation or geographical location.

The primary limitations of the probabilistic approach revolve around the availability of reliable and site-specific data such as porewater pressures and a detailed quantifiable understanding how geotechnical parameters may change over the life of the earthwork and the rate of this change.

Further work or studies could be undertaken to improve the reliability of data to better inform the approach and include:

• increase the quantity of effective stress triaxial shear strength tests in the database and undertaken in future ground investigations

• establish method for consistently defining the interpretation of shear strength parameters from the database

• improve formation specific correlations between plasticity and shear strength parameters

• undertake research on improved measurement of cohesion by laboratory testing or in situ tests

• undertake research to better quantify how shear strength parameters vary with time or the age of an earthwork

• increase greater extent of instrumentation and monitoring of porewater pressures over the life of the earthwork to directly assess changes in site-specific parameters.

3.3 Probabilistic Modelling of Earthwork Deterioration (‘Top Down’ Approach)

Aims and objectives

• This approach evaluates existing predictions of earthwork performance. The source of data for this is principal earthwork inspection observations held within the GDMS GAD data set.

• A check is undertaken to see whether forecast feature grade matches the actual condition 5 years after initial forecast.

• The objective of this work is to obtain an improved understanding of the reliability of current predictions.

• This approach will identify visible trends in the reliability of predictions for different defect types.

• The findings can be used to update the existing ‘Guidance Note on the ‘Field Identification and Classification of Geotechnical Observations’ to encourage a more consistent and reliable approach to assessing future performance.

• This approach seeks to improve the quality of performance assessments using existing methods, processes and data, as an interim measure.

Background

Principal earthwork inspections are undertaken in accordance with DMRB Volume 4 Section 1 Part 3 (HD 41/15) Geotechnics and drainage - Maintenance of highway geotechnical assets.

HD41/15 advises on best practice for the inspection and maintenance management of highway geotechnical assets. It provides guidance on geotechnical intervention and scheme prioritisation.



As part of HD 41/15, inspectors assign a Class and Location Index, which correspond to a Feature Grade to each earthwork Observation. In addition, they forecast the condition of the earthwork in 5 years’ time, resulting in a Forecast Feature Grade. At present, the Forecast Feature Grade is the only assessment of future performance that is made for earthworks assets.

The forecast is entirely dependent on the engineering judgement of the inspector on site. As such, there is a degree of subjectivity to this grading which is the focus of this investigation.

This investigation aims to analyse the predictions made, to identify any trends in the reliability of predictions for different defect types.

Following this analysis, suggestions for improving the reliability of deterioration predictions using current inspection methodologies and recommendations for amending current guidance can be given.

Data Processing

The data set used for this approach is the GAD data as received from Mott MacDonald. The data available covers inspections undertaken between the years 1995 and 2018.

This includes existing defect Observations and associated Forecast Feature Grades. Each record in the dataset corresponds to an inspection at a certain date. It is therefore required to process the data to allow for the comparison between Forecast Feature Grade at Year X, to the Initial Feature Grade at Year X - 5.

This was achieved by using database join and lookup operations. Three joins were carried out to represent differing cases:

• ‘literal’ – matching the Observations exactly 5 years apart;

• ‘fuzzy’ – matching the Observations 5+1 years apart;

• ‘next’ – matching to the next Observation irrespective of when it was carried out.

The amount of data resulting from each join increased where the join was less specific.

Table 3-6 summarises the size of the datasets resulting from each join. It can be seen that the ‘fuzzy’ join results in a dataset an order of magnitude greater than the literal join. Taking into account the guidance given in HD41/15 regarding the forecast period, whilst balancing the need to not reduce the dataset size too much, the ‘fuzzy’ join case was adopted for the analysis.

Principal inspections are typically carried out over the winter months, either side of Christmas. The ‘fuzzy’ join case would allow comparison in cases where inspection frequency was slightly over the 5-year repeat inspection timeframe interval. For example, this search would include a comparison of observations from December 2005 and January 2011, whereas the ‘literal’ join case would not. This ensures a reasonable population of pairs are compared, without straying too far from the typical five-year repeat inspection timeframe.

Table 3-6 – Volumes of data from joins

Join Type Number of rows in dataset Percentage

of total dataset set

Literal 3,392 0.7%

Fuzzy 31,524 6.8%

Next 234,359 50%

Total dataset size 464,592 -



Trends in Observation data

Appendix 3 contains plots displaying each of the defect types assessed for the analysis, split down by Highways England Area. Figure 3-4 below presents a box-and-whisker diagram summarising the plots found in the appendix. The figure presents all data from the lifespan of HA GDMS, with all Feature Grades having been calculated as per the HD41/15 methodology.

The following figures refer to field codes contained within the GAD extract. A reference of the definition of each of the field codes can be found in Figure 3-5. For the purposes of this analysis, a subset of only direct features descriptions have been analysed, with the further no change analysis being selected by the project team based on known defect types which can be problematic to make predictions on. Defects where the condition improved, e.g. due to a repair, were also removed from the further analysis.

Figure 3-4 - Top Down Approach



Figure 3-5 – Glossary of GAD extract field codes referenced in this document

Field Code Description

OB_D_SLIP Direct - Slip

OB_D_SLBUL Direct - Slope Bulge

OB_D_TENCR Direct - Tension Cracks

OB_D_DESIC Direct - Dessication

OB_D_TERR Direct - Terracing

OB_D_LEACH Direct - Leachate

OB_D_TODEB Direct - Toe Debris

OB_D_RAVEL Direct - Ravelling

OB_D_WEDGE Direct - Wedge Failure

OB_D_PLANR Direct - Planar Failure

OB_D_SUBSI Direct - Subsidence

OB_D_BADBF Direct - Poor Backfilled Excavation

OB_D_UNBF Direct - Unbackfilled Excavation

OB_D_SLIP_d Direct - Slip - No change removed

OB_D_TENCR_d Direct - Tension Cracks - No change removed

OB_D_SLBUL_d Direct - Slope Bulge - No change removed

OB_D_SUBSI_d Direct - Subsidence - No change removed

OB_D_DESIC_d Direct - Dessication - No change removed

Figure 3-4 indicates the accuracy of predictions in future performance. Where these include a prediction of ‘no change’ (left hand side of diagram), it can be seen that generally the predictions made are generally more than 80% correct, with a median of approximately 90%. This suggests that generally, predictions made on site correspond positively with the condition of the earthwork after 5 years.

When the Observations with no change are removed from the dataset (right hand side of Figure 3-4), the results change considerably. The percentage of correct predictions drops to generally around 30% (median value), with the ranges observed between managing Areas increasing significantly. This suggests that when predictions of active deterioration are made, the prediction is generally incorrect. As seen in the figure, it can be seen that there is a much greater spread in results across the Areas than the results with no change included.

A further manual analysis was carried out on the observations with no change and identified as slips, with the purpose of assessing whether the predictions over or under predicted the feature grade. This analysis showed that generally the incorrect predictions were under-predictions, such as a minor slip developing into a major slip, despite the prediction indicating that defect would remain similar.

Based on the results shown, and further analysis carried out, the following observations can be made:

• Predictions for rock slope defects tend to be less accurate than those for soil slopes;

• Predictions of deterioration of defects such as unbackfilled/poorly backfilled excavations are less accurate than other soil slope defects;

• There is a wide variation in the proportion of correct predictions per defect Class across the Areas, this is particularly visible when Observations with predictions of ‘no change’ are removed.

These conclusions indicate that it may be possible to target some types of defects to refine the guidance given to inspectors, with the aim of improving the consistency and accuracy of predictions, and potentially spending less time on site looking at less relevant defects.



Recommendations for good practice guidance

Currently, guidance for undertaking Principal Inspections is provided in “Guidance Note on the Field Identification and Classification of Geotechnical Observations”.

Considering our analysis, we would recommend that the following amendments are made to the sections outlined as below:

• Section 6: Timeframe o Add guidance outlining that the inspection repeat frequency must be influenced principally

by direct observations made by the inspectors whilst on site. This will allow an inspector to give an estimation on the rate of deterioration of a defect based on their observation.

o Potentially implement a post-inspection check (that could be run by Managing Agents or Highways England), which assesses whether the inspection frequency has been modified for assets where an observation with predicted deterioration is present.

• Section 8: Pessimism Bias o Wherever possible, one designated person of appropriate experience is to arbitrate all

Observations within each HE Area where a change in Feature Grade is predicted. This should ensure improved consistency with the existing check and review process across the network.

▪ Significant defects to be independent arbitrated by a designated person to ensure consistency across the SRN.

o This designated person should also oversee the inspectors to be sent out onto the network; and make appropriate selections based on the experience of the individual.

• Section 9: Non-Geotechnical Defects o Add text to emphasise that inspection time should be kept to an appropriate minimum

when assessing defects in the ‘excavation’ categories on site. o As an example, an ‘excavation badly’ backfilled filled with granular material is unlikely to

pose a significant hazard, whereas a 0.5m deep un-backfilled excavation located at the toe of a slope may do.

o There is only a need to spend significant time recording these if they have H&S implications, or directly affect the performance of highways infrastructure. This should be subject to the judgment of the inspector on site.

• Section 10: Animal Burrows o Add text to emphasise that inspection time must be kept to an appropriate minimum when

assessing defects in the burrowing categories on site. o Add improved definitions of what constitutes significant burrowing, i.e. large-scale rabbit

warrens / badger setts rather than single isolated burrows.

‘Top Down’ Conclusions

• Existing guidance on predicting future performance could be updated based on the Task findings.

• Identified trends in the reliability of predictions for different defect types, these can be seen in Figure 3-4 and Appendix 3.

• Established that the reliability of current deterioration predictions is highly variable, especially where active deterioration is likely.

• Practical limitations on the prediction of future performance have been identified to support scoping of future Tasks.

Work Package 3 Overall Conclusions

• The ‘Bottom Up’ approach has potential to provide Highways England with a repeatable and quantitative approach for modelling deterioration of earthworks. However, the present limits of data availability for the SRN and the data processing required presents challenges to the application of the method. It is possible that the methodology will act as a useful tool in the future, as the volume of available data increases.

• The ‘Top Down’ approach has retrospectively analysed the large existing data sets readily available through HAGDMS. Feature Grade predictions from Principal Inspections represent the



main existing means of predicting deterioration of earthworks. By identifying where the current process of predicting Feature Grade changes can be refined and improved through updated guidance, it is hoped that the quality of the data obtained through this process can be improved in the near future.



4. WP4 Development of Network Risk Areas for all asset types

Analysis of the condition information from HAGDMS has identified that, based on observation descriptions,

approximately 3% of SGMs are recorded as defective. The percentage of verified defective SGMs also

show that on a network scale, buttresses and scaling SGMs have a higher proportion of defects, with 44%

(8 of 18 SGMs) and 33% (2 of 6 SGMs), respectively, of those SGMs being defective across the whole

network. However, the total number of these SGMs recorded on the network is small, so the associated

proportions may not be statistically significant. All other verified defective SGMs were recorded to be below

15% of total network, with 10 SGM types having 10% SGMs verified to be defective. This shows that apart

from the few SGM types that have been identified here, the majority of the visible SGMs have no recorded

defects (97%).

20 SGM types have been recorded to have prevalent defects. There were no observable trends in the size

of defects associated with individual SGM types, with the length of defect shown to vary significantly. There

were also no observable trends in the nature of defects recorded for each SGM type, although this may

be related to the population size of the data available.

In October 2018, a workshop was held with Highways England Geotechnical supply chain and feedback

was obtained from those present regarding SGMs which are perceived as being of concern. Soil nails and

rock bolts were perceived to be of particular concern, with regards to performance, as well as sheet pile

walls due to potential corrosion. Referring to the review of the correlation between defects and SGMs

presented in Section 1.5 SGM Condition, rock bolts have a rate of approximately 1% verified defects

compared to the number of SGMs. Soil nails have 1% for the nails only, and 10% for the associated

meshing systems. Sheet pile walls had no verified defects within the dataset. This suggests that currently,

there is only limited correlation between perception of SGM performance and the available data on

performance of SGMs on the SRN. This mismatch may be due to individual respondents having a localized

view based on the part of the SRN they are most familiar with. However, it may also be the case that

perceptions are developed based on a small number of occurrences that are significant to the individual.

Anecdotally it also appears that, when defects requiring urgent attention arise on the network, whilst these

are well known to, and actioned by, the HE and supply chain staff involved, information relating to the

defects may be populated on HAGDMS retrospectively, and the available data may therefore not fully

represent the number or nature of current defects.

A summary of common defect types and typical associated physical changes has been provided to Task

1-477 to illustrate the nature of changes that would need to be detected by monitoring regimes.

Conclusions and recommendations

As part of the scope of Task 1-456, it was proposed to explore whether an ‘age’ could be established for

SGMs present on the network and automatically assigned. Assessment of the currently available data has

shown that such an approach is likely to result in very low confidence results being generated for a large

proportion of SGMs. This is as a result of a large proportion of the SGMs being installed as part of

improvement or remedial works, where the timing of these activities cannot be readily derived from the

available data. The exception to this is for SGM types that are typically only installed at original

construction, where the date assigned to the associated earthwork(s) could be adopted with a reasonable

degree of confidence. It is noted that more detailed information on the implementation of SGMs around

the network is now being captured, following recommendations from this and earlier Tasks. This will be

helpful in improving understanding of SGM performance for assets constructed from now on. However,

attributing ages to existing SGMs will still be required to support further work on performance. To achieve

this it may be necessary to associate SGMs with ‘Schemes’ and then to assign dates to Schemes based

on reports held in GAD.



It is recommended that the potential cause of a defect is recorded in observation descriptions in future

inspections to improve understanding of defect triggers. It is also suggested that a ‘best practice’ note is

also developed for those carrying out principal inspections to help improve the quality and relevance of

some of the information captured, along with the ability to positively associate defects with SGMs so that

a clearer picture of performance can be determined.

Anecdotal information suggests that some defects, whilst well known and managed, are only populated

in HAGDMS retrospectively. It is recommended that timely updates to HAGDMS are encouraged, so that

greater confidence can be achieved in statistics derived from the data.



5. Conclusions and recommendations

Overall, the tasks completed as part of Work Package 1 has successfully built upon methodologies set out

as part of Task 594. These have allowed the task to define a potential purpose for all SGMs identified

within the updated SGM inventory. The confidence with which potential purposes have been assigned to

SGMs is not currently very high, with approximately 77% of SGMs having a confidence rating of 4 or 5.

This is due to a lack of information that allows a direct association to be made.

Following the review of existing processes, the needs of Highways England, and the data available, it is

recommended that the assessment of performance and subsequent development of a framework to

classify and assess assets needs to be independently developed. However, it could follow in part that data

processing carried out as part of the Slope Hazard Rating and reported at network level following in a

similar fashion to the condition indicators. The process would be adapted to provide a rating of

performance of each asset, based on the weighted length of classified observations. The application of a

framework would allow an overarching review and comparison of performance, and consideration of the

anticipated performance derived in later work packages of this task.

As performance is specific to an asset, all classified observations should be considered, as this will include

and consider the impact of the asset to perform as it was intended, based on localised influences.

Figure 5-1: Aspect of each framework being taken forward to develop overall condition grade

It is anticipated, and recommended, that a framework Classification system A to E is developed based on

the score, which will provide a performance grade for each network asset. The index banding would be

applied based on distribution following a similar approach to the Slope Hazard Rating, whereby each asset

category has twice as many assets in it than the one before. The framework could also be applied to SGMs

as the anticipated performance of these is examined and understood better in future.

Work Package 3 progresses Task 1-456 from looking at current performance and SGMs to focus on future

condition and performance of geotechnical assets.

Currently, inspectors are required to predict the further deterioration of existing defects over a five-year

period as part of the Principal Inspection process. Analysis of these predictions has shown that they are

unreliable where deterioration is anticipated. As a short-term measure, to help improve the accuracy and

consistency of these predictions, recommendations have been made which could be cascaded to, and

reinforced with, the Area teams. However, predictions of this nature are inherently subjective and it is

apparent that even in Areas where good, consistent practice has been applied, the accuracy of predictions

is still variable.

This Task has also explored the potential to develop a new methodology for predicting future performance

which incorporates probabilistic deterioration modelling. This methodology has been applied to a localised

area of the network and has the potential to be applied more broadly. However, the approach is heavily

reliant on large quantities of digital geotechnical data being available, which is a constraint at the current

time. In addition, industry knowledge of how some geotechnical parameters vary over time is also limited

and will impact on the suitability of the models.

It is anticipated that, over time, the quantity of data readily available will continue to increase, and therefore

the proposed ‘bottom up’ approach will become easier to implement.

HD41/15 Class and Location Index

Slope Hazard Rating Score Methodology

A-E Condition

Index

Task 1-266 Descriptions

+ =

Performance Grade



Table 5-1 details aspects of the ‘bottom up’ approach in particular which would need additional work when

developing the methodology for application to the wider network.

Table 5-1: Summary of constraints associated with ‘bottom up’ methodology to be considered if applying to the wider SRN.

Aspect Description of associated constraint

Material variability Earthworks are comprised of a broad variety of materials which behave in very different ways, including granular and cohesive soils and rock, along with combinations of all of these. Earthwork materials naturally vary spatially, with such variations typically only partially visible via available testing.

Temporal variation in geotechnical parameters

Short and long-term parameters are typically derived for some earthworks materials. However, the variation in these parameters over time within varying materials and earthworks is less well understood. Analysis of parameter information which has been derived from ground investigations undertaken at the same site over long periods of time to obtain an indication of parameter variation with time would be beneficial.

Data for all geology types on the SRN

Currently large data sets are only available for certain geology types, i.e. London Clay. The creation of a geotechnical parameter database, which could be supported and accessed by a wider group of stakeholders, would be beneficial to the industry and all infrastructure owners. Such a database would improve our knowledge e.g. formation specific correlations between plasticity and shear strength parameters

Quantity of effective stress testing Increased quantities of effective stress triaxial shear strength tests in the database would be useful for future improvements in predicting earthworks performance. Consideration could be given to a general increase in scheduling such testing in future ground investigations

Consistency of testing Due to variations in laboratory methods and interpretation, tests on undisturbed samples were not used in this task. Consideration could be given to establishing a consistent method for interpreting shear strength parameters.

Fill material data Fill material will behave differently to in-situ materials of the same origin. Additional data is required to improve assessments of the deterioration of emplaced fills.

Drainage Drainage is integral to the performance of earthworks. Additional information is required to better understand the design-stage assumptions made in relation to earthworks drainage, current drainage condition, etc. to improve predictions of earthworks performance.

Vegetation The influence of vegetation (especially in clays) is significant in relation to earthworks stability. Further research is required (possibly linking in with existing academic work being undertaken) to assess the effects of vegetation on slope deterioration.

Age of SGMs Many SGMs have been invented within the last 20 years, so data are not available to allow accurate prediction of the performance of these techniques over the lifespan of an earthwork.

Nature and location of SGMs From a data perspective, the precise locations and arrangement of SGMs within an earthwork cannot be determined. The asset data model could be amended to allow more automated analysis of earthworks containing certain types of SGM.



Aspect Description of associated constraint

Historical earthworks performance Information on the historical performance of earthworks has been captured since the implementation of HD41 in 2003. However, this only provides information on a small duration of the lifespan of many assets, which compromises the ability to predict future performance based on historical behavior.

Defect causality Defects occur in earthworks for a variety of reasons, one of which is deterioration due to age. Current data does not typically define the cause of defects and, even in cases where forensic examinations are carried out as part of remedial works, determining causes is often still subjective.

Recommendations

Throughout this report, a number of recommendations have been made for potential future development and analysis.

• Investigation of defective SGM types – SGM types that have been identified as key SGMs that have been recorded as defective could undergo further inspection and analysis to understand why they are problematic.

• Investigation of buried SGM types – review existing testing/maintenance records, construction data and tacit information to define a methodology to review condition of buried SGMs where possible.

• Interaction with Task 1-447 – A summary of common defects and associated physical changes has been provided to the Proactive Monitoring Task. Further work could be carried out to develop trigger levels for specific SGM types, along with feedback from inspections to refine and validate the monitoring.

• Revisions to GAD – There is currently limited ability to input the purpose of the SGM in current GAD data, future revisions could include this as structured data and should also seek to improve the structure of the data to allow the presence of one or more SGMs and their relationship with associated earthworks to be more fully described.

• An updated guidance note should be produced on prediction of future performance to help improve consistency and, as far as is practicable, accuracy of these predictions. The note should also include commentary on how this will support the Condition Indicator work being undertaken as part of other tasks.

• Expansion of WP3 task, following initial proof of concept to explore application of the ‘Bottom Up’ approach for a wider variety of geological materials encountered on the Highways England network. This would however require substantial high-quality geotechnical data sets to be available for each geological material. For this reason, it would be recommended to start this process looking at materials which are commonplace on the network with large amounts of relevant ground investigation data.

• Continued involvement with academic projects which are studying earthworks deterioration and the effects of climate change such as Achilles. Key will be understanding how the outputs of such projects can practically be applied to the SRN.

• For stiff clay and stiff clay fills, vegetation plays a significant role in the progressive failure mechanism. Omitting the effects of this is a significant limitation. It would therefore be beneficial to support research to improve understanding of the influence of vegetation and how this may be influenced by climate change.

• Comparisons can be made between ‘Bottom Up’ analysis and existing work on Slope Hazard Rating. This could be undertaken to sense check both methodologies. The ‘Bottom Up’ analysis utilizes actual site-specific geotechnical parameter data rather than broader correlations.



• Development of a methodology for inspection and assessment of deterioration of rock slopes. This should be considered separately to soil slopes. Within existing GAD data, a clearer definition of where to use inspection criteria for soils and rocks is needed.



Appendix 1 – ‘Bottom Up’ Matrix

This appendix presents the matrix of results generated from the probabilistic slope stability analyses carried out as part of the bottom up approach.

Key to ‘Bottom Up’ Matrix

Term Description

ALL Global Dataset for London Clay Formation

J16-23 M25 Junction 16 to 23

LUS5 M25 Later Upgraded Sections

RapidWidening M25 Rapid Widening Section 4

ThamesTideway Thames Tideway Scheme (non-Highway tunnelling project)

Ru-CUT Porewater pressure coefficient (ru), cuttings

Ru-EMB Porewater pressure coefficient (ru), embankments

Cohesion-LT Cohesion, long term

Cohesion-ST Cohesion, short term

Site Ru Cohesion Height 1in2 1in2.25 1in2.5 1in2.75 1in3 1in4

ALL Ru-CUT

Cohesion-LT 10m 0.984 0.901 0.75 0.596 0.452 0.044

ALL Ru-CUT

Cohesion-LT 15m 0.986 0.909 0.754 0.612 0.461 0.049

ALL Ru-CUT

Cohesion-LT 2.5m 0.956 0.815 0.677 0.498 0.326 0.02

ALL Ru-CUT

Cohesion-LT 5m 0.974 0.874 0.731 0.568 0.422 0.033

ALL Ru-CUT

Cohesion-ST 10m 0.984 0.901 0.75 0.596 0.45 0.044

ALL Ru-CUT

Cohesion-ST 15m 0.985 0.908 0.754 0.612 0.461 0.049

ALL Ru-CUT

Cohesion-ST 2.5m 0.954 0.811 0.677 0.497 0.323 0.019

ALL Ru-CUT

Cohesion-ST 5m 0.974 0.874 0.729 0.567 0.419 0.033

ALL Ru-EMB

Cohesion-LT 10m 0.968 0.799 0.593 0.421 0.263 0.023

ALL Ru-EMB

Cohesion-LT 15m 0.975 0.813 0.606 0.435 0.277 0.028

ALL Ru-EMB

Cohesion-LT 2.5m 0.908 0.698 0.492 0.303 0.203 0.008

ALL Ru-EMB

Cohesion-LT 5m 0.955 0.764 0.557 0.391 0.248 0.017

ALL Ru-EMB

Cohesion-ST 10m 0.967 0.799 0.592 0.421 0.263 0.023

ALL Ru-EMB

Cohesion-ST 15m 0.975 0.811 0.606 0.434 0.277 0.027

ALL Ru-EMB

Cohesion-ST 2.5m 0.905 0.695 0.491 0.302 0.202 0.008

ALL Ru-EMB

Cohesion-ST 5m 0.951 0.763 0.556 0.39 0.247 0.017



J16-23 Ru-CUT

Cohesion-LT 10m 0.987 0.91 0.758 0.617 0.468 0.051

J16-23 Ru-CUT

Cohesion-LT 15m 0.989 0.915 0.769 0.626 0.477 0.063

J16-23 Ru-CUT

Cohesion-LT 2.5m 0.962 0.831 0.689 0.513 0.348 0.023

J16-23 Ru-CUT

Cohesion-LT 5m 0.979 0.887 0.74 0.585 0.437 0.038

J16-23 Ru-CUT

Cohesion-ST 10m 0.987 0.91 0.758 0.617 0.467 0.051

J16-23 Ru-CUT

Cohesion-ST 15m 0.989 0.915 0.769 0.626 0.476 0.062

J16-23 Ru-CUT

Cohesion-ST 2.5m 0.961 0.829 0.687 0.513 0.345 0.023

J16-23 Ru-CUT

Cohesion-ST 5m 0.979 0.886 0.737 0.584 0.436 0.038

J16-23 Ru-EMB

Cohesion-LT 10m 0.977 0.816 0.61 0.442 0.28 0.028

J16-23 Ru-EMB

Cohesion-LT 15m 0.982 0.835 0.631 0.453 0.288 0.031

J16-23 Ru-EMB

Cohesion-LT 2.5m 0.921 0.716 0.501 0.326 0.207 0.011

J16-23 Ru-EMB

Cohesion-LT 5m 0.963 0.784 0.572 0.408 0.258 0.019

J16-23 Ru-EMB

Cohesion-ST 10m 0.977 0.816 0.61 0.441 0.28 0.028

J16-23 Ru-EMB

Cohesion-ST 15m 0.981 0.834 0.63 0.453 0.287 0.03

J16-23 Ru-EMB

Cohesion-ST 2.5m 0.92 0.713 0.501 0.322 0.206 0.011

J16-23 Ru-EMB

Cohesion-ST 5m 0.962 0.779 0.572 0.407 0.257 0.018

LUS5 Ru-CUT

Cohesion-LT 10m 0.946 0.797 0.646 0.486 0.307 0.003

LUS5 Ru-CUT

Cohesion-LT 15m 0.951 0.808 0.656 0.495 0.317 0.004

LUS5 Ru-CUT

Cohesion-LT 2.5m 0.882 0.726 0.546 0.368 0.225 0.001

LUS5 Ru-CUT

Cohesion-LT 5m 0.927 0.772 0.616 0.46 0.281 0.002

LUS5 Ru-CUT

Cohesion-ST 10m 0.946 0.797 0.645 0.486 0.306 0.003

LUS5 Ru-CUT

Cohesion-ST 15m 0.951 0.808 0.655 0.495 0.317 0.003

LUS5 Ru-CUT

Cohesion-ST 2.5m 0.879 0.726 0.542 0.368 0.223 0.001

LUS5 Ru-CUT

Cohesion-ST 5m 0.926 0.772 0.616 0.459 0.279 0.002

LUS5 Ru-EMB

Cohesion-LT 10m 0.896 0.677 0.469 0.293 0.187 0.002

LUS5 Ru-EMB

Cohesion-LT 15m 0.906 0.684 0.479 0.299 0.193 0.002

LUS5 Ru-EMB

Cohesion-LT 2.5m 0.776 0.55 0.359 0.22 0.142 0.001



LUS5 Ru-EMB

Cohesion-LT 5m 0.858 0.629 0.437 0.268 0.177 0.002

LUS5 Ru-EMB

Cohesion-ST 10m 0.895 0.677 0.469 0.293 0.187 0.002

LUS5 Ru-EMB

Cohesion-ST 15m 0.905 0.683 0.479 0.299 0.191 0.002

LUS5 Ru-EMB

Cohesion-ST 2.5m 0.772 0.548 0.358 0.22 0.14 0.001

LUS5 Ru-EMB

Cohesion-ST 5m 0.857 0.628 0.432 0.267 0.176 0.002

RapidWidening Ru-CUT

Cohesion-LT 10m 0.978 0.874 0.728 0.555 0.402 0.023


Cohesion-LT 15m 0.98 0.88 0.735 0.565 0.411 0.028


Cohesion-LT 2.5m 0.932 0.778 0.629 0.465 0.283 0.005


Cohesion-LT 5m 0.97 0.842 0.697 0.532 0.37 0.015


Cohesion-ST 10m 0.977 0.874 0.727 0.555 0.401 0.023


Cohesion-ST 15m 0.98 0.88 0.735 0.564 0.41 0.028


Cohesion-ST 2.5m 0.932 0.777 0.624 0.463 0.28 0.005


Cohesion-ST 5m 0.97 0.842 0.694 0.53 0.367 0.015

RapidWidening Ru-EMB

Cohesion-LT 10m 0.959 0.753 0.543 0.373 0.238 0.01


Cohesion-LT 15m 0.965 0.764 0.556 0.381 0.243 0.014


Cohesion-LT 2.5m 0.872 0.646 0.448 0.272 0.18 0.003


Cohesion-LT 5m 0.934 0.725 0.506 0.336 0.217 0.006


Cohesion-ST 10m 0.959 0.752 0.543 0.372 0.238 0.01


Cohesion-ST 15m 0.965 0.764 0.555 0.38 0.241 0.014


Cohesion-ST 2.5m 0.869 0.644 0.443 0.27 0.179 0.003


Cohesion-ST 5m 0.932 0.723 0.506 0.335 0.216 0.006

ThamesTideway Ru-CUT

Cohesion-LT 10m 0.985 0.904 0.751 0.605 0.46 0.045


Cohesion-LT 15m 0.989 0.91 0.759 0.617 0.466 0.05


Cohesion-LT 2.5m 0.956 0.823 0.68 0.501 0.332 0.02


Cohesion-LT 5m 0.977 0.878 0.733 0.573 0.428 0.033


Cohesion-ST 10m 0.985 0.904 0.751 0.604 0.46 0.045


Cohesion-ST 15m 0.989 0.91 0.758 0.617 0.465 0.05




Cohesion-ST 2.5m 0.956 0.821 0.679 0.501 0.33 0.02


Cohesion-ST 5m 0.977 0.877 0.733 0.572 0.428 0.033

ThamesTideway Ru-EMB

Cohesion-LT 10m 0.972 0.806 0.6 0.43 0.271 0.027


Cohesion-LT 15m 0.978 0.821 0.611 0.439 0.28 0.028


Cohesion-LT 2.5m 0.912 0.702 0.495 0.31 0.204 0.009


Cohesion-LT 5m 0.961 0.772 0.561 0.395 0.249 0.018


Cohesion-ST 10m 0.972 0.803 0.6 0.43 0.27 0.026


Cohesion-ST 15m 0.977 0.82 0.611 0.438 0.28 0.028


Cohesion-ST 2.5m 0.91 0.7 0.494 0.309 0.204 0.009


Cohesion-ST 5m 0.96 0.769 0.561 0.395 0.248 0.018



Appendix 2 – ‘Bottom Up’ Figures

These figures present a geospatial comparison between the Bottom Up methodology and both known existing defects and the Slope Hazard Rating. All these plots have produced using the global distribution results.

M25 J23

Good correlation with

known existing defects

South Mimms

Service Station

Slope Hazard Rating Bottom Up Approach

Good correlation between

two methods

Differing results between

two methods at these locations



M25 J9 10

Good correlation with existing

known defects



two methods




known existing defects



two methods

Differing results between

two methods at these locations

M25 J24 25


known repair location



Appendix 3 – ‘Top Down’ Methodology Plots

This appendix reproduces the statistical plots produced as part of the top down approach, as well as some guidance on how the plots are intended to be read.

These plots have been used to help inform the conclusions presented in the accompanying report.

Guidance

Field Codes

GAD extract field codes may be referenced in this document, the table below provides a glossary of the field codes for reference

Field Code Description

OB_D_SLIP Direct - Slip

OB_D_SLBUL Direct - Slope Bulge

OB_D_TENCR Direct - Tension Cracks

OB_D_DESIC Direct - Dessication

OB_D_TERR Direct - Terracing

OB_D_LEACH Direct - Leachate

OB_D_TODEB Direct - Toe Debris

OB_D_RAVEL Direct - Ravelling

OB_D_WEDGE Direct - Wedge Failure

OB_D_PLANR Direct - Planar Failure

OB_D_SUBSI Direct - Subsidence

OB_D_BADBF Direct - Poor Backfilled Excavation

OB_D_UNBF Direct - Unbackfilled Excavation

OB_D_SLIP_d Direct - Slip - No change removed

OB_D_TENCR_d Direct - Tension Cracks - No change removed

OB_D_SLBUL_d Direct - Slope Bulge - No change removed

OB_D_SUBSI_d Direct - Subsidence - No change removed

OB_D_DESIC_d Direct - Dessication - No change removed

Box Plots

Where box plots are presented the following diagram indicates how they should be read.

Figure 2 - Box Plot Diagram

Outlier



Plots

This section will present each of the plots produced, with some accompanying text where appropriate.

Summary Plot

Figure 3 - Summary Plot



All Defect Plot

Figure 4 figure below plots the results when all data, irrespective of defect type is plotted together. The table below summarises the data calculated to produce the plot.

Figure 4 - All Defects Plot

EW_AREA FALSE TRUE Percent_Incorrect Percent_Correct Unique Obs Unique EWKs

1 67 1979 3.27% 96.73% 836 412

2 451 2713 14.25% 85.75% 2736 793

3 193 3015 6.02% 93.98% 2466 995

4 188 1223 13.32% 86.68% 1097 472

5 174 3956 4.21% 95.79% 3222 1153

6 184 1595 10.34% 89.66% 1338 488

7 268 2476 9.77% 90.23% 2235 970

8 523 2315 18.43% 81.57% 2391 747

9 282 2402 10.51% 89.49% 2135 790

10 165 2718 5.72% 94.28% 2541 1140

12 129 1656 7.23% 92.77% 1535 593

13 35 732 4.56% 95.44% 509 219

14 137 1538 8.18% 91.82% 1407 418

25 95 0% 100% 95 93

27 3 182 1.62% 98.38% 154 93

28 13 0% 100% 9 6

32 3 90 3.23% 96.77% 68 46

33 1 23 4.17% 95.83% 23 15

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

1

2

3

4

5

6

7

8

9

10

12

13

14

25

27

28

32

33

AR

EA

Percent_Incorrect

Percent_Correct



Slips Plot

The figure below plots the results for all defects identified to contain a slip feature. The table below summarises the data calculated to produce the plot.

Figure 5 - Slips Plot


1 7 84 7.69% 92.31% 46 30

2 96 392 19.67% 80.33% 445 140

3 65 649 9.10% 90.90% 576 239

4 29 186 13.49% 86.51% 156 91

5 66 868 7.07% 92.93% 848 315

6 75 381 16.45% 83.55% 329 126

7 65 591 9.91% 90.09% 514 273

8 77 307 20.05% 79.95% 342 144

9 98 672 12.73% 87.27% 596 285

10 76 598 11.28% 88.72% 620 321

12 53 444 10.66% 89.34% 453 201

13 16 262 5.76% 94.24% 178 85

14 60 578 9.40% 90.60% 535 155

25 11 11 11

27 2 65 2.99% 97.01% 54 36

28 4 3 2

32 1 23 4.17% 95.83% 19 10

33 1 11 8.33% 91.67% 12 6

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

1

2

3

4

5

6

7

8

9

10

12

13

14

25

27

28

32

33

AR

EA

Percent_Incorrect

Percent_Correct



Slope Bulge Plot

The figure below plots the results for all defects identified to contain a slope bulge feature. The table below summarises the data calculated to produce the plot.

Figure 6 - Slope Bulge Plot


1 4 55 6.78% 93.22% 24 16

2 75 389 16.16% 83.84% 407 118

3 42 341 10.97% 89.03% 326 135

4 29 159 15.43% 84.57% 147 80

5 41 348 10.54% 89.46% 355 137

6 41 249 14.14% 85.86% 215 99

7 24 237 9.20% 90.80% 209 131

8 55 248 18.15% 81.85% 268 124

9 50 411 10.85% 89.15% 369 184

10 49 226 17.82% 82.18% 250 122

12 25 127 16.45% 83.55% 142 70

13 7 137 4.86% 95.14% 96 47

14 28 378 6.90% 93.10% 350 104

25 2 2 2

27 29 24 19

28 2 2 1

32 1 23 4.17% 95.83% 18 9

33 3 3 3

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

1

2

3

4

5

6

7

8

9

10

12

13

14

25

27

28

32

33

AR

EA

Percent_Incorrect

Percent_Correct



Tension Cracks Plot

The figure below plots the results for all defects identified to contain a tension crack feature. The table below summarises the data calculated to produce the plot.

Figure 7 - Tension Cracks Plot


1 5 44 10.20% 89.80% 25 19

2 100 516 16.23% 83.77% 567 160

3 38 375 9.20% 90.80% 342 149

4 54 263 17.03% 82.97% 244 129

5 72 790 8.35% 91.65% 770 248

6 69 415 14.26% 85.74% 341 133

7 38 363 9.48% 90.52% 343 195

8 47 330 12.47% 87.53% 323 146

9 42 419 9.11% 90.89% 359 174

10 56 439 11.31% 88.69% 440 215

12 16 157 9.25% 90.75% 165 74

13 7 101 6.48% 93.52% 76 44

14 46 426 9.75% 90.25% 385 105

25 1 1 1

27 1 49 2.00% 98.00% 42 28

28 5 4 3

32 3 27 10.00% 90.00% 22 12

33 9 9 7

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

1

2

3

4

5

6

7

8

9

10

12

13

14

25

27

28

32

33

AR

EA

Percent_Incorrect

Percent_Correct



Dessciation Plot

The figure below plots the results for all defects identified to contain a desiccation feature. The table below summarises the data calculated to produce the plot.

Figure 8 - Dessication Plot


2 73 333 17.98% 82.02% 371 144

3 14 84 14.29% 85.71% 88 42

4 18 97 15.65% 84.35% 83 55

5 28 315 8.16% 91.84% 288 102

6 20 148 11.90% 88.10% 134 76

7 8 35 18.60% 81.40% 32 21

8 55 132 29.41% 70.59% 153 96

9 7 31 18.42% 81.58% 27 19

10 11 91 10.78% 89.22% 93 56

12 13 11 9

14 35 167 17.33% 82.67% 152 54

27 1 32 3.03% 96.97% 31 21

32 1 1 1

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

2

3

4

5

6

7

8

9

10

12

14

27

32

AR

EA

Percent_Incorrect

Percent_Correct



Terracing Plot

The figure below plots the results for all defects identified to contain a terracing feature. The table below summarises the data calculated to produce the plot.

Figure 9 - Terracing Plot


1 9 49 15.52% 84.48% 34 17

2 87 259 25.14% 74.86% 316 110

3 30 436 6.44% 93.56% 355 196

4 6 26 18.75% 81.25% 21 14

5 37 395 8.56% 91.44% 375 146

6 29 218 11.74% 88.26% 197 106

7 68 495 12.08% 87.92% 438 238

8 62 259 19.31% 80.69% 271 142

9 75 580 11.45% 88.55% 534 306

10 15 335 4.29% 95.71% 331 225

12 11 77 12.50% 87.50% 76 37

13 7 31 18.42% 81.58% 26 14

14 13 176 6.88% 93.12% 165 48

25 7 7 7

27 2 77 2.53% 97.47% 64 43

28 2 2 1

32 1 18 5.26% 94.74% 13 11

33 1 1 1

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

1

2

3

4

5

6

7

8

9

10

12

13

14

25

27

28

32

33

AR

EA

Percent_Incorrect

Percent_Correct



Toe Debris Plot

The figure below plots the results for all defects with the Toe Debris checkbox selected. The table below summarises the data calculated to produce the plot.

Figure 10 - Toe Debris Plot


1 8 33 19.51% 80.49% 28 15

2 12 70 14.63% 85.37% 66 25

3 9 131 6.43% 93.57% 112 64

4 2 44 4.35% 95.65% 34 22

5 4 111 3.48% 96.52% 103 41

6 15 74 16.85% 83.15% 67 20

7 9 84 9.68% 90.32% 78 48

8 21 73 22.34% 77.66% 85 35

9 19 134 12.42% 87.58% 127 62

10 12 67 15.19% 84.81% 73 50

12 5 40 11.11% 88.89% 39 23

13 2 25 7.41% 92.59% 20 14

14 5 39 11.36% 88.64% 40 18

25 4 4 4

27 26 21 16

32 3 2 2

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

1

2

3

4

5

6

7

8

9

10

12

13

14

25

27

32

AR

EA

Percent_Incorrect

Percent_Correct



Ravelling Plot

The figure below plots the results for all defects with the Ravelling checkbox selected. The table below summarises the data calculated to produce the plot.

Figure 11 - Ravelling Plot


1 18 82 18.00% 82.00% 56 31

2 26 174 13.00% 87.00% 163 48

3 13 149 8.02% 91.98% 122 74

4 2 11 15.38% 84.62% 9 5

5 3 123 2.38% 97.62% 113 57

6 6 53 10.17% 89.83% 41 26

7 27 242 10.04% 89.96% 221 148

8 10 39 20.41% 79.59% 42 22

9 23 258 8.19% 91.81% 218 123

10 1 36 2.70% 97.30% 36 22

12 3 55 5.17% 94.83% 53 33

13 29 15 6

14 1 5 16.67% 83.33% 5 4

25 3 3 3

27 39 30 22

33 3 2 2

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

1

2

3

4

5

6

7

8

9

10

12

13

14

25

27

33

AR

EA

Percent_Incorrect

Percent_Correct



Wedge Failure Plot

The figure below plots the results for all defects with the Wedge Failure checkbox selected. The table below summarises the data calculated to produce the plot.

Figure 12 - Wedge Failure Plot


1 4 16 20.00% 80.00% 14 7

2 7 6 3

3 10 7 5

4 1 6 14.29% 85.71% 5 3

5 30 28 7

6 6 5 3

7 2 10 16.67% 83.33% 8 5

8 1 1 1

9 1 8 11.11% 88.89% 9 7

10 2 2 1

12 1 8 11.11% 88.89% 9 7

13 7 5 4

14 4 2 1

25 4 4 4

27 2 2 1

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

1

2

3

4

5

6

7

8

9

10

12

13

14

25

27

AR

EA

Percent_Incorrect

Percent_Correct



Planar Failure Plot

The figure below plots the results for all defects with the Planar Failure checkbox selected. The table below summarises the data calculated to produce the plot.

Figure 13 - Planar Failure Plot


1 3 23 11.54% 88.46% 17 11

2 1 14 6.67% 93.33% 11 3

3 20 20 13

5 2 24 7.69% 92.31% 24 8

6 17 13 8

7 3 13 18.75% 81.25% 13 8

8 1 6 14.29% 85.71% 7 6

9 9 8 6

10 1 1 1

12 6 14 30.00% 70.00% 20 11

13 4 3 2

14 2 1 1

27 2 2 1

33 1 1 1

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

1

2

3

5

6

7

8

9

10

12

13

14

27

33

AR

EA

Percent_Incorrect

Percent_Correct



Subsidence Plot

The figure below plots the results for all defects with the Subsidence checkbox selected. The table below summarises the data calculated to produce the plot.

Figure 14 - Subsidence Plot


1 6 58 9.38% 90.63% 36 19

2 67 185 26.59% 73.41% 230 60

3 39 443 8.09% 91.91% 385 184

4 40 181 18.10% 81.90% 176 103

5 43 518 7.66% 92.34% 503 185

6 53 259 16.99% 83.01% 237 125

7 40 189 17.47% 82.53% 198 124

8 104 345 23.16% 76.84% 359 159

9 61 298 16.99% 83.01% 290 169

10 12 89 11.88% 88.12% 95 52

12 14 89 13.59% 86.41% 92 64

13 54 0.00% 100.00% 37 19

14 21 226 8.50% 91.50% 207 104

27 9 0.00% 100.00% 8 8

28 1 0.00% 100.00% 1 1

32 3 29 9.38% 90.63% 27 17

33 3 0.00% 100.00% 3 3

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

1

2

3

4

5

6

7

8

9

10

12

13

14

27

28

32

33

AR

EA

Percent_Incorrect

Percent_Correct



Poorly Backfilled Excavation Plot

The figure below plots the results for all defects with the Poorly Backfilled Excavation checkbox selected. The table below summarises the data calculated to produce the plot.

Figure 15 - Poorly Backfilled Excavation Plot


2 1 12 7.69% 92.31% 8 6

3 1 36 2.70% 97.30% 31 23

4 3 8 27.27% 72.73% 10 6

5 1 35 2.78% 97.22% 34 19

6 4 0.00% 100.00% 4 4

7 1 6 14.29% 85.71% 7 7

8 4 4 50.00% 50.00% 7 4

9 10 0.00% 100.00% 7 6

10 4 87 4.40% 95.60% 88 72

12 1 7 12.50% 87.50% 7 7

13 15 0.00% 100.00% 9 6

14 2 30 6.25% 93.75% 32 21

27 7 0.00% 100.00% 7 7

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

2

3

4

5

6

7

8

9

10

12

13

14

27

AR

EA

Percent_Incorrect

Percent_Correct



Unbackfilled Excavation Plot

The figure below plots the results for all defects with the Unbackfilled Excavation checkbox selected. The table below summarises the data calculated to produce the plot.

Figure 16 - Unbackfilled Excavation Plot


1 2 9 18.18% 81.82% 9 5

2 2 15 11.76% 88.24% 15 11

3 1 84 1.18% 98.82% 63 54

4 4 0.00% 100.00% 4 4

5 3 69 4.17% 95.83% 67 38

6 10 0.00% 100.00% 10 7

7 3 7 30.00% 70.00% 9 9

8 2 0.00% 100.00% 2 2

9 3 38 7.32% 92.68% 33 27

10 115 0.00% 100.00% 113 60

12 1 19 5.00% 95.00% 18 17

13 9 0.00% 100.00% 5 5

14 1 46 2.13% 97.87% 41 29

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

1

2

3

4

5

6

7

8

9

10

12

13

14

AR

EA

Percent_Incorrect

Percent_Correct



Slip – Detailed Plot

The figure below plots the results for all defects with the Slip checkbox selected, and where no change has been recorded between the two inspections. The table below summarises the data calculated to produce the plot.

Figure 17 - Slip - Detailed Plot


1 2 100.00% 0.00% 1 1

2 40 13 75.47% 24.53% 51 16

3 12 15 44.44% 55.56% 24 14

4 4 1 80.00% 20.00% 4 3

5 24 7 77.42% 22.58% 30 21

6 21 6 77.78% 22.22% 25 17

7 9 5 64.29% 35.71% 14 13

8 13 5 72.22% 27.78% 18 13

9 21 12 63.64% 36.36% 28 19

10 14 4 77.78% 22.22% 17 15

12 2 1 66.67% 33.33% 3 3

13 8 2 80.00% 20.00% 8 5

14 30 27 52.63% 47.37% 51 20

27 1 1 50.00% 50.00% 2 2

33 1 1 50.00% 50.00% 2 1

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

1

2

3

4

5

6

7

8

9

10

12

13

14

27

33

AR

EA

Percent_Incorrect

Percent_Correct



Tension Cracking– Detailed Plot

The figure below plots the results for all defects with the Tension Cracking checkbox selected, and where no change has been recorded between the two inspections. The table below summarises the data calculated to produce the plot.

Figure 18 - Tension Cracking - Detailed Plot


2 31 19 62.00% 38.00% 49 25

3 17 9 65.38% 34.62% 22 7

4 8 5 61.54% 38.46% 9 5

5 21 9 70.00% 30.00% 29 21

6 33 8 80.49% 19.51% 33 16

7 7 3 70.00% 30.00% 9 8

8 17 1 94.44% 5.56% 18 12

9 4 8 33.33% 66.67% 10 8

10 8 1 88.89% 11.11% 8 7

12 1 0.00% 100.00% 1 1

13 5 100.00% 0.00% 4 3

14 14 29 32.56% 67.44% 37 17

27 1 1 50.00% 50.00% 2 2

32 2 100.00% 0.00% 1 1

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

2

3

4

5

6

7

8

9

10

12

13

14

27

32

AR

EA

Percent_Incorrect

Percent_Correct



Slope Bulge– Detailed Plot

The figure below plots the results for all defects with the Slope Bulge checkbox selected, and where no change has been recorded between the two inspections. The table below summarises the data calculated to produce the plot.

Figure 19 - Slope Bulge - Detailed Plot


1 2 100.00% 0.00% 1 1

2 20 10 66.67% 33.33% 29 15

3 11 10 52.38% 47.62% 20 11

5 17 4 80.95% 19.05% 21 14

6 13 2 86.67% 13.33% 14 10

7 5 1 83.33% 16.67% 6 5

8 7 2 77.78% 22.22% 9 6

9 8 7 53.33% 46.67% 14 10

10 8 100.00% 0.00% 7 6

12 1 1 50.00% 50.00% 2 2

13 2 2 50.00% 50.00% 3 2

14 16 18 47.06% 52.94% 32 13

27 1 0.00% 100.00% 1 1

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

1

2

3

5

6

7

8

9

10

12

13

14

27

AR

EA

Percent_Incorrect

Percent_Correct



Subsidence– Detailed Plot

The figure below plots the results for all defects with the Subsidence checkbox selected, and where no change has been recorded between the two inspections. The table below summarises the data calculated to produce the plot.

Figure 20 - Subsidence - Detailed Plot


2 31 15 67.39% 32.61% 42 12

3 14 7 66.67% 33.33% 20 9

4 6 10 37.50% 62.50% 13 9

5 10 3 76.92% 23.08% 13 9

6 19 1 95.00% 5.00% 18 7

7 10 5 66.67% 33.33% 12 11

8 20 4 83.33% 16.67% 24 16

9 26 2 92.86% 7.14% 24 19

10 1 1 50.00% 50.00% 2 2

12 2 2 50.00% 50.00% 4 4

14 11 3 78.57% 21.43% 13 8

32 2 100.00% 0.00% 1 1

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

2

3

4

5

6

7

8

9

10

12

14

32

AR

EA

Percent_Incorrect

Percent_Correct



Desiccation– Detailed Plot

The figure below plots the results for all defects with the Desiccation checkbox selected, and where no change has been recorded between the two inspections. The table below summarises the data calculated to produce the plot.

Figure 21 - Desiccation - Detailed Plot


2 16 15 51.61% 48.39% 31 15

3 9 2 81.82% 18.18% 9 5

4 2 6 25.00% 75.00% 6 5

5 4 1 80.00% 20.00% 5 4

6 6 100.00% 0.00% 6 2

7 2 100.00% 0.00% 2 2

8 12 100.00% 0.00% 10 7

10 1 0.00% 100.00% 1 1

14 1 7 12.50% 87.50% 7 4

27 1 100.00% 0.00% 1 1

0.00% 20.00% 40.00% 60.00% 80.00% 100.00%

2

3

4

5

6

7

8

10

14

27

AR

EA

Percent_Incorrect

Percent_Correct

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