Bridge Management A System Approach for Decision … · 2010 67 Bridge Management a System Approach for Decision Making Reginald W. Stratt (PhD) Postdoctoral student at the Department

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Bridge Management a System Approach for Decision Making

Reginald W. Stratt (PhD)Postdoctoral student at the Department of Engineering and Technology in the

School of Doctoral Studies of the EUSquare de Meeus 37-4th Floor 1000 Brussels, Belgium

email: [email protected]

Abstract

Bridges in the United Kingdom (UK) are subject to various physical processes that result in their deterioration over time. They can be constructed using different materials such as concrete or steel, or in combination with other materials, with varying number of spans and are used to carry different levels of loads. Inevitably, bridges deteriorate over time at different rates and they are vulnerable to varying forms of deterioration (e.g. rusting, corrosion). Timely maintenance activities that are well-planned and carried out with minimal disruption to road users can present substantial savings in terms of both time and money for both bridge owners and road users. As a result, the likelihood of disruptive emergency maintenance will be reduced and subsequent maintenance costs over the service life of bridges will not build up significantly due to neglect.

To tackle the complicated issues regarding bridge management, research activities in the UK as well as other countries in continental Europe concentrate largely on the bridge management process, with attention given to improving the use of limited finances so as to maximize the returns from the maintenance and repair of the bridge stock as well as reduce additional costs due to traffic delays and lane closures for these activities.

This project aims to first appreciate current bridge management systems (BMS), understand the practices used for determining bridge conditions and aid the decision-making process by using a systems approach. The study includes a critical review of other BMS’s used worldwide, development of models to predict bridge condition over time, analysis of the various road user costs and using different optimizing techniques to best allocate finances and optimize bridge performance.

Key words: Bridge Management, Decision Making.

1. INTRODUCTION

Bridges are subject to various forms of deterioration in the environment, with different rates of deterioration depending on the degree of exposure, location, type of bridge and extent of use of the bridge. The main causes of deterioration are: corrosion of steel; alkali-silica reaction; concrete carbonation; frost damage; sulphate attack; and structural damage from vehicle impact. Corrosion of reinforcing steel in concrete bridges also causes cracking, staining and spalling of concrete, that

in turn accelerates the deterioration process. Insufficient waterproofing, or failure of these systems can also allow salt concentrations to exceed the threshold required for corrosion. Spray from road traffic can cause deterioration of adjacent bridge elements that are both above and below the road surface.

In addition, bridge movement joints are highly susceptible to corrosion due to flowing of saline water over the sub-structure and subsequent pooling. In addition to the direct cost of engineering work carried out to maintain the bridge stock, indirect costs due to

Stratt R. S. - Bridge ManagementA System Approach for Decision Making

Bridge Management A System Approach for Decision Making

School of Doctoral Studies (European Union) Journal - 2010

68 School of Doctoral Studies (European Union) Journal 2010

traffic congestion can be significant. Therefore, these factors need to be considered in order to achieve good bridge management (Znidaric A and Moses F, 1997).

Formed in 1994, the Highways Agency (HA) was formed in the United Kingdom (UK) with the purpose of managing, maintaining and improving the network (Baker, D, 1999). It acts as an executive agency to the Department of Transport (DoT). The HA is responsible for the maintenance of approximately 9500 kilometers of motorways and trunk roads. Maintenance agents are contracted by the HA to inspect, review, design and carry out repair work in order for bridges to attain an acceptable level of performance and safety. In the year 2001-2002 alone, for example, about £500 million was spent, averaging about £56 000 per kilometer. Over the last eight to ten years, approximately £750 million has been spent annually. In 1994, a study by Transport Research Laboratory for the DoT found that personal injury accident rate was a hundred and thirty per cent higher on sites with roadworks (Wallbank E J, 1986).

Because of this study, reducing the risk of accidents and their consequential costs to other road users has become one of the HA’s top priorities with regards to carrying out maintenance or repair work. The HA’s Structures Management Information System (SMIS) is a bridge management system that is used to help engineers manage structures and it adheres to established codes of practice (i.e. British Standards, Design Manual for Roads and Bridges (DMRB)). Its processes include planning and inspection procedures, identification of needs and forming projects after prioritizing and evaluating various maintenance options. The SMIS can store new input data over the duration of maintenance activities, hence SMIS users are able to review their progress.

In the UK, several bridge condition rating and bridge scoring systems are currently practiced. Much research is currently underway to create a version that can be standardized and implemented nationwide. The rating or scoring systems are generally based on individual bridge element condition, appearance, importance of the bridge, severity and extent of deterioration. Recommendations work for repair, replacement or maintenance can then be translated into costs, which the engineer can use to form and manage a list of tasks required. However, other consequences of their decisions such as additional costs imposed on road users due to lane closures, effects of road accidents as a result of inadequate maintenance,

delays during maintenance, impacts on the environment and changes in bridge condition over time are often not taken into account. Professional judgment is often relied upon to decide which alternatives to select for implementation (Znidaric A and Moses F, 1997).

After gaining an understanding of current practices and approaches used in bridge management for the UK and other countries, this project will consider some of the latest research techniques undertaken by the HA and their maintenance agents to model the conditions of bridges at the individual level as well as the bridge stock level, to assist in local decisions as well as nationwide strategic decision-making. This project will have models developed to estimate costs borne by road users due to traffic congestion and traffic accident costs, subject to different scenarios. The impacts on the environment due to increased emissions will be also addressed and estimated accordingly. Finally, Markov chain approaches will be considered to provide a means to achieve optimal budget allocation to better manage the bridge stock and optimize the performance levels for individual bridges.

2. OBJECTIVE

The main objective of this project is to understand the current bridge management practices and use them to estimate the current state of bridges as well as the bridge stock and model their condition over time. It will relate bridge scoring results with different types of costs and develop a framework that can incorporate this information with optimizing strategies to aid engineers and bridge managers in the decision-making processes concerning bridge management.

The following tasks need to be carried out:

1. Appreciate the current bridge conditions of the bridge stock in the UK.

2. Understand the typical allocations of budget for various bridge maintenance activities.

3. Learn about the different BMS types, including their structure and information used.

4. Understand bridge inspection techniques, how key elements are assigned rating scores and assessing deterioration.

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3. BRIDGE CONDITIONS IN THE UK

3.1 Introduction

The Bridge Management In Europe programme (BRIME) was a project started by the Forum of European National Highway Research Laboratories (FENHRL) in January 1998 that seeks to improve bridge management in Europe by improving the structure of managing bridges in the European road network. In a recent survey reported in the BRIME report, the composition of UK’s bridges is as follows:

Figure 3 1 Distribution of bridge types in the UK in 1997 (Woodward R J et al, 1999)

Bridge type distribution (1997)

4%13%

3%

43%

28%

9%

Steel & Iron

Composite Steel

Stone

Concrete & ReinforcedConcretePrestressed Concrete

Other

Figure 3 2 Distribution of bridge age in the UK in 1997 (Woodward R J et al, 1999)

Bridge age distribution (1997)

30%

65%

5%

<20 years20 to 40 years>40 years

Figure 3 3 Distribution of bridge length in the UK in 1998 (Woodward R J et al, 1999)

Bridge length distribution (1998)

68%

25%

7%

<50m50 to 100m>100m

Figure 3 4 Distribution of bridge types in the UK over 50 years (Woodward R J et al, 1999)

As seen from above, a majority of the bridge stock in the UK comprises of concrete and reinforced concrete bridges (43%), with 65% of the bridge stock having the age of 20 to 40 years and of length less than 50m (Woodward R J et al, 1999). This has implications of more attention being needed to manage the problems

5. Understand the scoring system for bridges and bridge stock.

6. Develop models to estimate road user costs due to maintenance and accidents .

7. Develop an emission model to investigate environmental impacts due to congestion.

8. Develop simple programmes to optimize budge allocation.

9. Develop models to optimize bridge performance.

10. Use sensitivity analysis to deal with uncertainties.



Figure 3 5 Average extent ratings for key bridge elements from 1998 to 1994

From 1998 to 1994, it can be observed that the proportion of bridges in the bridge stock having no significant deterioration has been declining gradually, with just over 50% of them in 1994. This is also the same for those having less than 5% of the element affected. The proportion of bridges suffering from extensive deterioration (i.e. more than 20% of the element affected)

Figure 3 6 Average severity ratings for key bridge elements from 1998 to 1994

Similarly, the average severity ratings from 1988 to 1994 has gradually decreased over time in general, with about 2% of the bridge stock described as having severe defects needing urgent attention.

3.2 Maintenance & Upgrading

Routine maintenance is carried out with the aim of prolonging the asset life making up the road network. Most of these works are planned (about 90%) (Bourn J, 2003). Appropriate action at the right time can avoid future damage and in turn save more money, such as repairing cracks on the road surfaces to reduce frost penetration over the winter months, or resurfacing of badly rutted roads to increase the safety levels by providing a good road surface. Other maintenance activities include routine work such as inspections, cleaning, painting, drain clearing and lubrication of bridge bearings.

that concrete and reinforced concrete bridges face. Age-related maintenance problems need to be addressed as well, such as bridge strengthening due to increased vehicle weight and increased usage. A significant proportion of bridges are between 50 to 100m in length, which could mean more severe traffic congestion consequences should bridges of these lengths need emergency repair due to unexpected failure, or need to be closed after a serious accident. Modelling of traffic delays and costs can help the bridge manager deal with these issues and these are addressed later on.

is on the increase with approximately 7% in 1994 (almost doubling since 1988’s 4%).

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Figure 3 7 Distribution of the HA’s expenditure from 1988 to 1995 (Bourn J, 1995)

HA expediture (April 1988-March 1995)

71%

10%

19%

Steady state maintenance

Upgrading

Assessment &strengthening

In 1987, the DoT started a fifteen-year programme aiming to improve the condition of the motorway, trunk road bridges and other structures to a ‘good’ condition. The total cost of the programme was estimated to be £2.2 billion. In 1995, a review of the expenditure was made and it can be seen that a majority of the expenditure by the HA from 1998 to 1995 was spent on steady state maintenance. Approximately £500 million had already been spent within the first seven years (Bourn J, 1995).

Bridge upgrading deals with specific bridge elements that need improvement in terms of safety and durability, such as waterproofing unprotected and vulnerable areas, replacing unsafe parapets and reinforcing collision areas to absorb the impact of vehicles. A more detailed breakdown in expenditure can be seen as follows:

Figure 3 8 Distribution of bridge expenditure in 1989 (Bourn J, 1989)

3.3 Expenditure





Comparision as % of cost (1989)

0

10

20

30

40

50

60

Design Specifications Supervision Workmanship

Element

Cost

,mill

ions

RoadsBridgesTotal

Figure 3 9 Comparison of expenditure as a percentage of cost

From Figure 3-8, Figure 3-9 has been created. This is a summary of comparisons of design, specification, supervision and workmanship costs made as percentages of the total expenditure on roads, bridges and as a total respectively. For example, the amount of money spent on design as a percentage of the money spent on roads is approximately 45%. For the amount of money spent on bridges, design costs take up approximately 55% and for the total expenditure; design costs make up about 53% of the total.

It should be noted from Figure 3-8 that should problems arise due to more than one factor, repair costs are divided equally among the various elements of expenditure, hence the total will exceed the actual amount spent. For roads, a large proportion of expenditure is spent on design and specifications, whereas for bridges the expenditure is largely attributed to design followed by workmanship. The proportional expenditure to meet specifications for roads is significantly higher compared to bridges and the total expenditure for both roads and bridges. This could be explained by the many different specifications set out for different types of roads that need to be met, depending on location, ground conditions, environment, community and so on.

Compared with bridges, specifications are more clearly set out using British Standards and other codes of practice and would include standard requirements of meeting load bearing capacity, sufficient moment resistance and so on. Supervision regarding remediation

problems for roads and bridges are almost the same, as these usually involve experts (e.g. highway and road maintenance agencies) that are knowledgeable in both fields. This element of expenditure is generally consistent (approximately 20% of expenditure) and therefore money spent for roads or bridges do not differ largely compared to the proportional total expenditure.

The breakdown of expenditure by the HA from 1994 to 2002 is as follows:

Figure 3 10 Distribution of the HA’s expenditure for various works from 1994 to 2002

Although the total amount of money spend by the HA has decreased since 1994-1995 to 2001-2002, this is attributed largely to the decrease in number of new road construction. This can be explained by the fact that the construction of the required new roads have been completed and has subsequently reduced to simply replacing those that are decommissioned annually. It should be noted that the amount of money spent on road and bridges maintenance has generally been steady and more money is spent to pay Design Build Finance Operate (DBFO) operators (Bourn J, 2003). These two observations are attributed to an increasing number of ageing bridges needing more work done on them with less work required for the new brides built several years earlier.


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Figure 3 11 Breakdown of the HA’s expenditure components for 2001 to 2002

In the period 2001-2002, £502 million was spent by the HA, largely on capital maintenance and routine maintenance. As the number of new road construction was low, a portion of the £32 million was for this cause. Since DBFO type roads make up 7% of the network, a reasonable proportion of the HA’s expenditure has been channelled towards paying managing agents and other maintenance related fees (Bourn J, 2003).

Figure 3 12 Cost of capital maintenance per square meter (1997 to 2002)

The cost of capital maintenance for 2001-2002 is about £37.50 per square meter of road, adjusted for inflation. This is translates to about a 10% increase from previous years, which are about £32.50 per square meter (Bourn J, 2001).

3.4 Department of Transport (DoT) Programme

The DoT spearheaded a fifteen-year programme in 1987 to improve the road network that included motorways, bridges and other structures. With the HA acting as their executive agency, their responsibilities include improving and implementing standards regarding network improvement.

Three types of key sub-programmes are carried out:

1. Assessing and strengthening of structures to carry vehicles up to thirty-eight tonnes and forty tonnes from 1999 onwards

2. Carrying out maintenance work, with a developed programme for such activities

3. Upgrading structures such that they meet the required design standards set out for them

With standards being improved and made more rigorous over time, a larger number of elements inspected received a lower condition rating. It was noted that new structures built twenty to twenty-five years ago, required their full maintenance, hence resulting in a large amount of spending later on 1994 (about £1 billion) (Bourn J, 1989).

In May 1983, the weight limit for lorries in the UK was increased from thirty-two and a half tonnes to thirty-eight tonnes (Bourn J, 1988), but just over a year later in December 1984, the European Council of Minister approved a Directive to increase the gross vehicle weight limit to forty tonnes, significantly exceeding the UK’s thirty-eight tonnes limit established a year before. As a result of negotiations, the UK delayed increasing the thirty-eight tonne limit to forty tonnes till 1999. This was a 23% increase from thirty-two and a half tonnes by comparison, which pose serious implications for old structures designed to carry lighter vehicle loads. Between 1984 and 1986, a review of short span bridges spanning less than 50m in length by the DoT suggested





that up to 22% this bridge type would not pass new assessment codes used then. About a thousand old short span motorways and trunk road bridges were estimated to require strengthening work or total replacement. The initial load bearing assessment of structures was done by mathematical modelling, failing which would subject the bridge to more tests and investigations.

By comparing the HA’s Structures Database with that of the Organization for Economic Development (OECD), it can be seen that there is scope for improving integration of information using modern computational tools. There is a current general lack of data and access, with little information concerning bridge management work. Obtaining and managing information can improve the planning and supervision of inspection or maintenance activities, with better usage of time to minimize delays and consequential costs incurred (OECD Report, 1992). Integrating traffic and accident information to the database can develop better management of traffic congestion and allow actions to be taken early so as to avoid delays or reduce its severity. There is a need to optimize and manage budgets and other costs by appropriate a systems approach to aid decision-making.

Figure 3 13 Comparison of the HA’s NSP Database with OECD model BMS

4. WHAT IS A BRIDGE MANAGEMENT SYSTEM (BMS)?

4.1 Introduction

A bridge management system (BMS) is an organized approach to handle the various needs and requirements regarding good stewardship of bridges. This usually involves a software or other database to deal with the large amount of data involved and display them on both element and strategic level for the user to aid the decision-making process (Clausen P, 1992). The main functions of a typical BMS are:

1. To provide a full inventory of bridges, with record and predictions of both the past and future condition of bridge elements and components. This also applies to the bridge’s load carrying capacity.

2. To assess the deterioration rate, select the optimal maintenance strategy based on cost effectiveness and evaluate the cost of other alternatives.

3. To determine the traffic delay costs in order to improve traffic management.

4. Calculate the discounted costs to obtain the life cycle cost.

5. Consider the safety and congestion issues when maintenance activities are underway.

6. Plan for a maintenance programme that is optimized and prioritized.

7. Aid the planning of subsequent budgets.

Bridges can be grouped together based on different criteria, such as their location (i.e. region), connection with routes, age, condition (e.g. substandard), number of spans and construction material (e.g. concrete, steel).

4.2 Department of Transport Structure

The Department of Transport (DoT) is responsible for the design and implementation of the national roads policy for the UK and also involves determining the budgets and targets that need to be attained. It provides the funds


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for the HA, its executive agency for management of motorways, trunk road bridges and other structures and also aids local highway authorities financially to manage their local road assets. Of the six main Directorates that make up the HA, the Network Management and Maintenance, Civil Engineering and Environmental Policy, Finance and the Roads Programme Directorate oversee the bridge management elements of programme management, allocation of funding to regions and maintenance agents, coordination and planning work as well as managing them (Bourn J, 1989).

There are two main types of maintenance agents, who are local authorities (e.g. City of London) and consultant engineering firms (e.g. Halcrow). The duration of contracts are typically about five years. The agents are responsible for carrying out both inspections and assessments on a regular basis. Although local authorities tend to have long term arrangements with the HA and consultant engineering firms who previously obtained bridge and road maintenance work through bidding are now beginning to develop long term agreements with the HA as well (e.g. Mouchel Parkman). A diagrammatic representation of the structure of the DoT is as follows:

Figure 4 1 Structure of the Department of Transport (1995)

4.3 Maintenance Agents

The HA bears the responsibility for the bridge programmes set out by the DoT, however the other works are usually handled by maintenance agents who have agreements with the HA regarding such work. Once the required work has been finished, a claim is submitted to the HA by the local authority. The remaining amount of money, after advance payment has been deducted, is obtained from the next advance payment. Engineering consultants, on the other hand, submit invoices to the HA and are then paid by arrears when work has been completed. Depending on the complexity of bridges and scope of maintenance work, the costs incurred will undoubtedly vary from agent to agent.

Considering the various costs incurred for six maintenance agents in the figure below, it can be seen that the costs of maintenance per square meter varies (e.g. significant costs for agent A). In addition to simply awarding the maintenance contract to the lowest bidder, it is important for the HA to match each agent’s specialist skills and technical knowledge to the type of bridge maintenance work, so as to minimize waste incurred from inexperience and unnecessary delays. However, there are other factors not easily identified or quantified, such as the relationships that the HA has developed over time with the agents through past experiences.

Figure 4 2 Expenditure per square meter of bridge work (1988 to 1994)

4.4. BMS in the UK & Other Countries

4.4.1 Introduction

From the Bridge Management In Europe programme (BRIME), a review of the bridge management systems (BMS) used in the UK, Europe and other countries





can be made. In order to better understand the types of BMS’s and their approaches, comparisons between them can highlight advantages and disadvantages of each BMS. The countries included in this comparison are France, Germany, the UK, Norway, Slovenia, Spain, Denmark, Finland, the USA and Canada (Calgaro J A, 1997, Woodward R J et al, 1999).

Eight of the ten countries listed above use a computerized form of BMS, except for Germany and Spain (Der Bundesminister fur Verkehr, 1982). France uses a partially computerized BMS (Calgaro J A, 1997) and the BMS’s vary between two to twenty years in age. Countries that have a computerized BMS generally use the commercially available ORACLE software as their database, with the UK and Slovenia as exceptions (Znidaric J et al, 1995). There are no clear guidelines are drawn out for bridge management in Slovenia, compared to other countries having documents that give instructions concerning this. The UK uses a maintenance manual and France uses a type of management instruction. Norway, Denmark, Finland and USA (New York) have user manuals provided (Andersen N H, 1990, Børre S, 1997). Germany and Spain both do not have any special guidelines for their BMS’s (Vorshrift 169/89, 1989).

4.4.2 Bridge Condition

Generally, there are three to four levels of inspection, ranging from routine to special. The results are stored in the database used, however both the UK and Spain do not store the condition results of bridges. Germany only stores the overall condition result for the whole bridge, without the condition results of individual elements (Naumann J, 1998). The condition scale is usually a three to five level rating scale.

4.4.3 Other Information in BMS’s

For each of the ten countries, the date, type, cost and location of maintenance work is stored in the BMS. Only the BMS of Denmark and Canada record the bridge condition immediately before and after maintenance has been carried out (Andersen N H, 1990, Road Directorate & Denmark Ministry of Transport, 1995).

4.4.4 Condition Prediction

Most countries do not use a deterioration model to predict future bridge conditions. However, Finland that uses a probabilistic Markovian model for the network and a deterministic model for individual projects (Söderqvist M K and Veijola M, 1998). USA (New York) and Canada uses past condition data in their deterioration models (Yanev B S, 1998), whereas France and Slovenia uses past ratings instead (Znidaric J et al, 1995).

4.4.5 Cost Models

Most BMS’s are used to record the costs for maintenance, repair and inspections, except for Germany, Norway and Slovenia (Børre S, 1997). They generally do not consider that costs of disruptions to road users as a result of maintenance or repair work. In the UK, traffic delay costs are either computed by referring to tables or using QUADRO, a computer programme for this purpose.

4.4.6 Decision for Maintenance & Repair

Most countries do not base their decisions concerning maintenance and repair on the BMS’s. Denmark uses a prioritization method (Andersen N H, 1990) and Finland uses a repair index (Söderqvist M K and Veijola M, 1998). Canada uses a software (PONTIS) to obtain cost optimal strategies. The UK uses cost benefit analysis and France and Germany rely on engineering judgment. Decisions for maintenance work and the selection of options generally hinge on inspections and engineering judgment. Choices made in the UK are dependent on the alternatives available and the cost of traffic delay. For larger, more important projects, a whole life costing approach is used for evaluation (DETR, 1998).

4.4.7 Prioritization

The UK’s BMS does not create any optimizing strategy for attaining the minimum acceptable level of performance of bridges for the minimum cost. However, it takes into account of policies when strategies are being

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formed. Finland, Canada, USA (New York), Spain and Denmark uses optimization techniques that can handle budget constraints and Denmark and USA (New York) calculate the consequences of not selecting the optimal strategy (Lauridsen J and Lassen B, 1998).

Figure 4 3 Information stored on various BMS’s (Woodward, R J et al, 1999)

4.5 BMS in the USA

In the USA, three types of BMS systems are used, namely PONTIS, BRIDGIT and State Specific Systems (i.e. States that develop their own BMS). PONTIS is the most common system, used by thirty-nine states. The BRIDGIT programme is used in the state of Maine, Washington and Louisiana as a project level system. Alabama, Indiana, New York, North Carolina and Pennsylvania have developed their own BMS’s.

4.5.1 BRIDGIT System

From a set of alternatives that can meet the required level of performance, BRIDGIT provides a means to select the best optimal solution. This is achieved by an extensive model that can review the condition states of bridges, determine policies that allocate funds optimally and recommend appropriate maintenance activities. BRIDGIT is used as a tool to manage transport investments, plan projects and oversee programmes. The type of database used is called FoxPro or Visual FoxPro. Both individual bridges as well as bridge stock can be managed using the information in the database (Hawk H and Small E P, 1998).

Inspection information is reviewed every bi-annually and special inspections are carried out periodically on structures classified as high risk of failing to meet design

standards. Visual inspection uses a three to five scale rating system and is stored in the database. The date, type and cost of maintenance work carried out over each bridge are recorded and the pre-maintained and post-maintained bridge condition is stored. For predicting the future state of bridges, BRIDGIT employs a Markovian deterioration model that uses a transition probability matrix approach. The overall condition of a bridge is based on the worst condition derived from these analyses. In addition, BRIDGIT can record costs borne by road users due to traffic delays, accidents, increased travel time and distances as a result of maintenance activities (Hawk H and Small E P, 1998).

It should be noted that life cycle costs are calculated based on a twenty-year horizon and the expected benefits are quantified on the basis of user cost savings. Prioritization and planning of projects use incremental cost benefit analysis methods, which can work out the optimal cost (minimum) given a budget constraint.

4.5.2 PONTIS System

PONTIS uses a “health index” to measure the performance of bridges. It is possible to calculate the future health index of a bridge based on the size of budget available. PONTIS uses an infinite planning horizon when determining the optimizing the expected life cycle costs and the benefits are based on potential savings by delaying maintenance action by a year. Since PONTIS’s objectives are to reduce the costs incurred for bridge elements and overall expenditure, the prioritization takes on a minimal cost approach that is dictated by the acceptable level of performance. Bridge engineers can use PONTIS to create reports and graphical presentations for the wider purposes of long-term planning, budgeting and forecasting (Thompson P D et al, 1998).

4.6 SMIS System

The Structures Management Information System (SMIS) is a new database developed by the Highways Agency (HA) to help achieve better maintenance and operation of the road network in the UK. This system aims to provide a better integration of information which is simple to maintain, update and use, both from the engineer as well as the client’s point of view. SMIS is networked with all sixteen of the HA’s managing





agents by the agency’s extranet and information traffic is handled by a dedicated HA server (HA, 2003). It also allows access via the Internet.

The four key processes of the SMIS is as follows:

1. Planning works.

2. Input inspection data and sign-off (i.e. technical and budge approval).

3. Defining maintenance actions and forming projects.

4. Link to HA Management Information System (HAMIS) for bidding & works

SMIS is capable of many sub-level activities and these are discussed in more detail below.

4.6.1 Inventory

A database of information regarding bridges, culverts, tunnels, retaining walls and other related data such as photographs and drawings

4.6.2 Inspection & Assessment

Structures undergo a general inspection every two years and a principal inspection every six years. Inspection results are recorded in SMIS and the different types of recommendations proposed are recorded into the database. The severity of the defect detected as well as the extent is stored at the element level.

4.6.3 National Structures Programmes (NSPs)

NSPs aim to meet policies regarding the rehabilitation of structures on a national level and as such interact with the maintenance needs proposed after inspections. For a bridge strengthening programme set out by NSPs, SMIS can be used also to monitor progress and track costs.

4.6.4 Prioritization

Risk-based prioritization can be used to make decisions concerning maintenance activities. Taking into account the consequential probability of an event occurring if remedial actions are delayed and its consequences, a risk scoring method can be employed.

The effects of safety, functionality, sustainability and the environment are reviewed and given scores. By their combinations, a prioritization exercise aids the decision-making process. Engineering judgment is required for decision-making and the SMIS does not undertake any of these responsibilities as it is meant to act as a tool to aid judgment for good structures management.

4.6.5 Project Creation

SMIS is able to create new projects using a combination of possible maintenance activities. The various alternatives generated can then be chosen based on the minimal project cost, minimal whole life cost, or full specification criteria (HA, 2003). For a specific activity, a report can be generated by SMIS to provide current knowledge for the user regarding it (e.g. a report of all the structures that need new sound barriers installed). This fast and easy access to specific activities can help engineers understand the implications of their actions, such as traffic delay due to traffic diversion, a lane closure or a full bridge closure. The flexible use of SMIS in this way provides a means to visit all possible alternatives and obtain estimates of expected costs.

4.6.6 Whole Life Assessment & Costing

The net present value (NPV) of maintenance activities can be calculated based on the types of decisions taken to rehabilitate a faulty element of a bridge structure. The cost of traffic delays is also considered in this module of whole life assessment.

4.6.7 Activities Schedule

A calendar of event records the various activities that are being planned, with the ability of calling up reports for activities that are overdue or delayed. Inspections that have not taken place for more than two years and the number of high risk activities that are taken can also be called up for review (HA, 2003).

4.6.8 Data Accuracy

It is important that data be continually updated and accurate to maintain data integrity. When a project or individual activity has been completed, SMIS is able to prompt for portions of the database that needs to be

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updated. This allows the database to be more interactive with the user.

4.6.9 Design Specifications

There is a requirement for approval in the SMIS by the relevant technical parties, so that the designs of structures adhere to standard codes of practice and will not be in danger of any breaches.

4.6.10 Access

SMIS operates with a password requirement for all users and varying levels of access are given depending on the type of user operating it.

4.6.11 Integration with External Systems

SMIS is able to interface with other external systems of the HA, enabling better links to manage finances, data, projects on a wider scale. Linking up with the Project Finance Information System, SMIS helps the collating process of costs in the NSPs (HA, 2003). Data from the project finance system also gives feedback to users of the SMIS, facilitating better coordination. SMIS provides the Pavement Management Information System with data regarding structures, allowing the possibility of carrying out resurfacing or repair works concurrently.

Interface with the HA’s Graphical Information System (GIS), the SMIS provides data on structures so that the information can be mapped out and its distribution be shown on a national level as well as overlaid onto the road network. Any changes from standard practice can be updated in the SMIS, making certain that such changes will be highlighted to the user. Finally, the SMIS is linked with the Accounting System and by giving updates quarterly, it facilitates continual reviews of the HA’s financial commitments (HA, 2003). Examples of how SMIS records defects, bridge elements and generate reports are shown in the Appendix.

4.7 Bridge Inspection & Assessment

Bridge inspections are typically carried out by maintenance agents that are appointed by the HA. There are various types of inspections, as well as extent of detail covered. Simple visual inspections are used to assess the general condition of bridges, but more

detailed procedures such as testing of samples, using proof loads, are usually directed towards specific studies (Thoft-Christensen P, 1996). From these inspections, the assessment of the bridge or bridge stock can be made and appropriate maintenance activities can be planned for after allocating the needed funds.

Assessments of the bridges structural capacity are carried out to evaluate its load bearing capacity, which could be affected if the maximum unladen weight of vehicles is increased. There are predominantly 5 levels of assessments, increasing in order of detail and complexity (Woodward R J et al, 1999). They are as follows:

1. Assessment carried out by consulting code prescribed methods. This process is basic and follows a similar manner to that of the design of a bridge.

2. A more detailed analysis is carried by means of structural models. The same type of partial safety factors as the first method is used.

3. This level of assessment uses more detailed load and resistance values of the bridge. Material strengths can be analyzed and code prescribed reductions in partial safety factors can be implemented.

4. A study using reliability approach can be carried out, with more modifications to partial safety factors, depending on the load and resistance requirements.

5. Full reliability analysis with probability outputs.

4.7.1 Bridge Inspection Types

There are four kinds of inspections for bridges in the UK and can be used in combination depending on inspection needs (HA, 1994). The types of inspections are as follows:

1. Superficial inspection of bridges by maintenance agents of the HA. Outstanding defects that pose a risk to safety are highlighted and action taken immediately to remedy them.

2. General visual inspection of bridge elements that are easily accessible. These need to be carried out at least every two years after a general or principal inspection.





3. A principal inspection of all bridge elements, including those that are difficult to gain access. These may sometimes require specialist access machinery or tools for inspection. Such an inspection should be carried out at least every six years and about one month before a new bridge is commissioned for use.

4. Special inspections involve detailed investigation of a particular bridge component. This can suffice when they have been excessively loaded at a point in time, or have recently undergone a serious traffic accident. Bridges that have been strengthened using plates bonded to them also require special inspections.

Key bridge elements that are included in bridge inspections are: foundations, piers, columns, retaining walls, embankments, bearings, slabs, waterproofing material, expansion joints and so on.

Figure 4 4 Key bridge elements for inspection

Figure 4 4 Examples of inspected bridge types and primary deck elements (ATKINS, 2002)

4.8 Defects

Defects detected from inspections are classified using a code prescribed procedure set out by the HA. Severity codes are used in a scale of one to five, describing the degree of deterioration, from minor (structurally sound) to a collapsed state (non-functional). They cover a host of types of structures as well as aspects of the bridge’s condition. Not all need to be included in the assessment of a bridge, as some do not apply.

Codes are available to record defects for the following bridge properties (HA, 1994):

1. Metalwork

2. Reinforced Concrete, Prestressed Concrete & Filler Joist

3. Masonry, Brickwork & Mass Concrete

4. Painting & Protective Coats

01 - solid spandrel arch 02 - open spandrel arch 02 - braced spandrel arch

03 - tied arch (bowstring) 04 – Prestressed beams with deck slab

04 – Prestressed beams and deck slab with pseudo voided slab 04 – Prestressed beams and deck

slab with solid concrete infill

05 – Prestressed box beams

with deck slab 05 – Prestressed box beam

06 - half through girder 07 – Filler beam with deck slab(steel beams

with concrete infill)

08 - underslung truss 09 - half through truss 10 - through truss(cross

members above vehicles)

Figure ¡Error! No hay texto con el estilo especificado en el documento.-1 Examples of inspected bridge types and primary deck elements (ATKINS, 2002)

2010 81

5. Vegetation

6. Foundations

7. River bed

8. Drainage

9. Bridge Surfacing

10. Expansion Joints

11. Embankments

12. Bridge Bearings

13. Impact Damage

14. Waterproofing

Pictorial examples of defects for different bridge types, in varying degrees of deterioration are shown in the Appendix.

5. BRIDGE SCORING

5.1 Introduction

In a recent publication (ATKINS, 2002) by the County Surveyors’ Society (CSS) and WS Atkins Consultants (two organizations that research on strategic planning, transportation, environment and waste management and economic issues for the HA), a proposed bridge scoring system takes into account the conditions of bridge elements, the degree of their contribution to bridge integrity and the contribution of bridges to the overall bridge stock condition.

Scoring methods may be used to classify bridge elements according to their importance and condition. It is then input into an algorithm that can derive the overall bridge condition. By building up a standardized system, the condition of the bridge stock can be assessed and reviewed by the bridge manager. The iterative process is as follows:

1. Select a bridge, select an element

2. Assign an Element Condition Score (ECS)

3. Assign an Element Importance Classification & Factor (EIF)

4. Assign an Element Condition Factor (ECF)

5. Compute the Element Condition Index

6. Repeat 1 to 5 for next element, otherwise proceed

7. Compute the Bridge Condition Score (BCS)

8. Compute the overall Bridge Condition Index (BCI)

9. Repeat 1 to 8 for next bridge, otherwise proceed

10. Compute the Bridge Stock Condition Index (BSCI)

5.2 Definitions

The definitions of terms used in the scoring system:

Table 5 1 Definition of terms used in scoring system (ATKINS, 2002)

1 Element Condition Score (ECS) Scale of 1 to 5 (1 for best, 5 for worst) for severity & extent

2 Element Importance Factor (EIF) Accounts for the value of the element, defined as “Very High”, “High”, “Medium”, or “Low”

3 Element Condition Factor (ECF)Accounts for an element’s contribution to overall bridge condition. Range of values depend on element importance (see Table 5-6)

4 Element Condition Index (ECI) Element condition using ECS & ECF5 Bridge Condition Score (BCS) Scale of 1 to 5 for the bridge condition6 Bridge Condition Index (BCI) Scale of 0 to 100 after conversion from BCS

7 Bridge Stock Condition Index (BSCI)

Condition of bridge stock, considers the deck area of each bridge

Extent Severity

1 2 3 4 5

A 1.0

B 1.0 2.0 3.0 4.0

5.0C 1.1 2.1 3.1 4.1

D 1.3 2.3 3.3 4.3

E 1.7 2.7 3.7 4.7

Table 5 2 Element Condition Score (ECS) (ATKINS, 2002)

*Shaded boxes represent non-permissible Severity/Extent combinations





In table above, the severity is defined as the degree to which the defect or damage affects the bridge element on the bridge. The extent is defined as the length, area, or number of defects or damages of the bridge element. The shaded boxes in the table represent scenarios that are not permitted as there cannot be severity greater than 1.0 with extent A (“no significant defect”). The extent codes and severity descriptions are defined as follows:

Table 5 3 Extent Descriptions for ECS (ATKINS, 2002)

Code DescriptionA No significant defectB Slight, not more than 5% of surface area/length/numberC Moderate, 5% - 20% of surface area/length/numberD Wide: 20% - 50% of surface area/length/numberE Extensive, more than 50% of surface area/length/number

Table 5 4 Severity Descriptions for ECS (ATKINS, 2002)

Code Description1 As new condition or defect has no significant effect on the element

(visually or functionally).2 Early signs of deterioration, minor defect/damage, no reduction in

functionality of element.3 Moderate defect/damage, some loss of functionality could be expected 4 Severe defect/damage, significant loss of functionality and/or element is

close to failure/collapse5 The element is non-functional/failed

The various definitions for EIF, ECF and ECI are as follows:

Table 5 5 Element Importance Factor (EIF) (ATKINS, 2002)

Element

Importance

EIF

Very High 2.0

High 1.5

Medium 1.2

Low 1.0

Table 5 6 Expressions for Element Condition Factor (ECF) (ATKINS, 2002)

Element

Importance

Element Condition Factor

(ECF)

Very High ECF = 0.0

High

Medium

Low

( )[ ]4/3.013.0 ×−−= ECSECF

( )[ ]4/6.016.0 ×−−= ECSECF

( )[ ]4/2.112.1 ×−−= ECSECF

Table 5 7 Element Condition Index (ECI) (ATKINS, 2002)

ECI = ECS – ECF

The BCS is computed based on taking into account the contribution of each bridge element, weighted accordingly depending on their importance. Hence, key bridge elements contribute to a larger degree to the computed BCS using the definition below, as a low ECI for an important element with reduce the BCS greater compared to using an element with lower EIF with the same ECI.

There are two types of BCS’s, namely BCS (average) and BCS (critical). The first is involved with all the bridge elements that contribute to the overall score, while the latter considers the bridge elements that are of very important to the bridge’s ability to provide safety and durability. The two types of BCS’s are computed as follows, with ‘critical’ or ‘average’ used in the subscripts accordingly (ATKINS, 2002):

( )

∑

∑

=

=

×= N

ii

N

iii

Average

EIF

EIFECIBCS

1

1

Where N is the total number of bridge elements used.

=

beam nghead/cappi-crossfor ECInpier/columfor ECI

cantileveror beamparapet for ECIbeam/rod for tie ECI

joints halffor ECIelementsdeck secondary for ECI

elementsdeck primary for ECI

maxCriticalBCS

2010 83

It should be noted that BCS (average) could be misleading to the actual condition of a bridge, as a bridge can be at a high risk of failure after experiencing severe damage to a specific component during a traffic accident. For example, a bridge can have an important element damaged (i.e. high EIF and low ECI) after a severe accident. However, with other bridge elements in good condition, the computed BCS (average) still indicates a “good” condition. Thus, the BCS (critical) considers the maximum ECI for bridge elements by using the ‘weakest link’ principle to identify such scenarios. The BCS (critical) does not fully reflect how deterioration is spread over the bridge, hence both types of BCS’s should be considered.

5.4 Bridge Condition Index (BCI)

In order to better represent the BCS, the Bridge Condition Index (BCI) is used, defined on a scale of 0 (worst) to 100 (best). The determined BCS is converted into a BCI. Similarly to the average and critical BCS values, the BCI (average) and BCI (critical) can be obtained. The relationship between BCI and BCS is as follows with the appropriate BCI (average) or BCI (critical) values substituted respectively (ATKINS, 2002):

( ) ( ){ }5.75.62100 2 −×+−= BCSBCSBCI

Figure 5 1 Plot of Bridge Condition Index (BCI) & Bridge Condition Score (BCS)

BCI & BCS

0

20

40

60

80

100

1 2 3 4 5

BCS

BCI

By plotting out the relationship between BCI and BCS as shown above, it can be observed that the BCI decreases at an increasing rate as the BCS increases. This indicates that their relationship is not perfectly linear.

5.5 Bridge Stock Condition Index (BSCI)

In addition to considering the condition of structures on a bridge level, a broader perspective needs to be achieved for effective bridge management. The Bridge Stock Condition Index (BSCI) suitably fulfils this need. To correctly reflect the contribution of each bridge, the size of each bridge should be considered. Hence the deck areas are used to weight each bridge’s contribution. The BSCI (average) and BSCI (critical) are computed on a scale of 0 (worst) to 100 (best) and its method is as follows (ATKINS, 2002):

( )

∑

∑

=

=

×= M

ii

M

i

DeckArea

iDeckAreaBCIBCSI

1

1

Where M is the total number of bridges in the bridge stock and the deck area is computed as:

1. Area = Overall width * centerline to centerline distance of end supports, or,

2. Area = Overall width * distance between each face of the end supports + 0.6m

For the same reasons for BCS, both average and critical values need to be considered, so as to capture impacts of a critical bridge influencing the bridge stock and also weighting the BCI by the size of the bridge using the deck area.

5.5.1 Multi Span Bridges

For a multi span bridge, inspections can be made for each span. The BCI is then as follows (ATKINS, 2002):

( )AreaDeck Bridge Whole

AreaDeck Span 1∑=

×=

S

ii

BCIBCI

Where S is the total number of spans of the bridge.Spans in multi span bridges are typically inspected

and rated individually, hence the BCI is weighted using the span deck area and computed in this way to consider the contributions of individual spans to the BCI. An important span taking up a larger proportion of the whole





bridge deck area will invariably have a greater influence to the computed BCI.

5.6 Bridge Scoring Example

Mr Paul Williams, Route Manager for the HA, had been contacted for real sample bridge scoring data. However, he had declined to assist owing to security requirements, and his email can be found in the Appendix. Nevertheless, using the recommend approaches to determine the BSCI (average) and BSCI (critical), original examples have been created to study the interactions between the various indicators and scores on the individual bridge level as well as the bridge stock. The computations have been carried out in Excel and can be found in the Appendix. The virtual bridge stock consists of seventy-three bridges that are made up of twenty dual span bridges and thirty-three single span bridges.

The following steps have been taken to create a virtual bridge stock containing seventy-three bridges for investigation in this project:

1. Several ‘bridges’ need to be created for a bridge stock. Firstly, ten typical bridge elements are selected and assumed scores are assigned to them (ECS). Different deck areas for each span are assumed (e.g. 1000m2) and to make this exercise more realistic, these ‘typical’ bridges are analyzed as being made up of both single and dual span bridges.

2. The bridges are put into three assumed classes with each class having a generalized set of input data mentioned earlier. The three classes reflect the overall condition of bridges, as ‘good’ condition, ‘medium’ and ‘bad’. The ECS values selected reflect these classes accordingly (i.e. lower scores for the ‘good’ bridges than the ‘bad’ bridges’).

3. The bridge stock is then generated by means of multiplying each class by an assumed number to create more bridges.

4. Using the BSCI computation, the BSCI (average) and BSCI (critical) can be computed which takes into account the contribution of large and smaller bridges, as well as the number of such structures.

The ten selected bridge elements used in this exercise are:

1. Steel beams

2. Columns

3. Foundations

4. Bearings

5. Waterproofing

6. Painting

7. Road surface

8. Retaining walls

9. Embankments

10. Footway surface

5.6.1 Interpreting BCS’s

The BSCI gives guidance to the general condition of the bridge stock and works as a tool for developing bridge management strategies. By comparing BSCI values with other local authorities or at a regional level, progress can be charted accordingly and target performance can be planned for.

For the purposes of managing individual bridges, a histogram can plot the distribution of BCI’s, or BCS’s by bridge types, spans, or any other suitable category to highlight decisions that need to be taken. The choice to use the BCI or BCS depends on the user’s preference, as engineers who have a better understanding of BCS may use that instead and help others understand better by using the BCI. The BCI ranges from 0 (not functional) to 100 (best) and the BCS ranges from 1 (best) to 5 (worst). It should be noted that the BCI operates on a linear scale, whereas the BCS measures deterioration on a non-linear scale, hence more difficult to understand entirely. The following remarks for BCS (average and critical) are as follows:

2010 85

BCS (Average)

(All Bridge Elements)

BCS (critical)

(Worst Critical Element)

1.0 → 1.3

No significant defects in any elements;

Bridge is in a "Very Good" condition overall.

Insignificant defects/damage;

Capacity unaffected.

1.31 → 1.8Mostly minor defects/damage;

Bridge is in a "Good" condition overall.

Superficial defects/damage;

Capacity unaffected.

1.81 → 2.7

Minor-to-Moderate defects/damage;

Bridge is in a “Fair” condition overall;

One or more functions of the bridge may be significantly affected.

Minor defects/damage;

Capacity may be slightly affected.

2.71 → 3.7

Moderate-to-Severe defects/damage;

Bridge is in a "Poor" condition overall;

One or more functions of the bridge may be severely affected.

Moderate defects/damage;

Capacity may be significantly affected.

3.71 → 4.7

Severe defects/damage on a number of elements;

One or more elements may have failed;

Bridge is in a "Very Poor" condition overall.

Possibly element failure;

Severe defects/damage;

Capacity may be severely affected;

Bridge may need to be weight restricted or closed to traffic

4.71 → 5.0Majority of bridge elements have failed;

Bridge is unserviceable.

Failure of critical element;

Bridge should be closed.

Table 5 8 Interpretation of BCS’s (ATKINS, 2002)

The outputs for the generate bridges in the spreadsheet exercise have been classified according to the three different bridge types defined. Bridge type 1, 2 and 3 represent bridges in good, medium and poor conditions respectively.

5.6.2 BCS Results

The bridge condition score (BCS) values have been computed and plotted together, shown below:

Figure 5 2 Plot of BCS’s for each bridge span for each bridge type in the bridge stock

BCS's for each span for each bridge type

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Bridge Type 1 Bridge Type 2 Bridge Type 3Bridge Stock

BC

S Va

lue BCS 1st span(avg)

BCS 1st span(crit)

BCS 2nd span(avg)

BCS 2nd span(crit)

It can be seen that for the bridges that are classified to be in the ‘bad’ state, the BCS (average) values for both the 1st and 2nd spans are close to their respective BCS (critical) values. Considering the BCS (average)

values, bridge type 1 comes under the ‘very good’ and ‘good’ interpretation as their BCS (average) are between 1.0 and 1.5. For bridge type 2 in the bridge stock, their BCS (average) values range between 1.5 and 2.0, which puts them in the ‘good’ and ‘fair’ category for the 1st and 2nd span respectively. For the last bridge type, the BCS (average) values are between 3.0 and 3.5, putting both span conditions in the ‘poor’ category.

For the bridge type that receives a good condition score, the differences between the BCS (average) and BCS (critical) are larger, compared to a smaller difference when using the bridge types that score poorly. This is due to the fact that the poorly scored bridges are also near their critical score, meaning that their lower limit is about to be exceeded due to a critical element in risk of failing, thus work is required urgently. Such an observation is expected, since bridges in a poor condition will be invariably closer to a critical state than those in a good condition.

5.6.3 Histograms for Bridge Stock

Since the Bridge Stock Condition Index (BSCI) provides only a single output value, much information may be lost and it could be misrepresentative of the state of bridges. Using histograms to plot the distribution of bridge condition for the bridge stock can better achieve clear representation of the current state of the stock.

In order to generate more ‘bridges’ in the bridge stock, another spreadsheet that considers single span bridges is used to create them. The spans for dual span bridges are considered as separate ‘bridges’ since they have separate computed scores and are inspection separately. The BCS values when combined with that of those for the dual span bridges, generate the BCS (average) histogram below for a bridge stock of seventy-three ‘bridges’ (twenty dual span bridges and thirty-three single span bridges):

Figure 5 3 Histogram plot of BCS (average) values

BCS (average) Histogram

6

24

1513

10

5

0 00

5

10

15

20

25

30

1.0 - 1

.5

1.5 - 2

.0

2.0 - 2

.5

2.5 - 3

.0

3.0 - 3

.5

3.5 - 4

.0

4.0 - 4

.54.5

- 5

Range

No.

of b

ridge

spa

ns





From the histogram above, it can be observed that the majority of bridge spans are classified to be well within the lower half of the BCS range, indicative of a fairly healthy bridge stock. However, since some or these are in the ‘poor’ classification, remedial work can then be planned and carried out to create a phase shift towards the lower BCS range. Statistical approximations can be fitted to model the distribution of BCS’s, such as a gamma distribution. A normal distribution is not expected to be a good fit, due to imposed lower bound value of 1.0 and upper bound value of 5.0. Similarly, the BCS (critical) histogram is obtained:

Figure 5 4 Histogram plot of BCS (critical) values

BCS (critical) Histogram

21

37

15

00

10

20

30

40

1.0 - 2

.0

2.0 - 3

.0

3.0 - 4

.0

4.0 - 5

.0

Range

No.

of b

ridge

spa

ns

Approximately twenty-five per cent of the bridge spans are between the ranges of 3.0 – 4.0, indicating that there is a significant proportion experience reduced load carrying capacity due possible element failure or moderate damage. The BCS (critical) indicates the contribution of key elements to the overall condition score and hence for these bridge spans, imposing load limits may be required, pending further structural analysis of the bridges. Using histograms to display information gives more scope of detailed statistical analysis if necessary, as well as serves as a useful tool in budgeting and sharing information to other non-technical parties.

5.6.4 Interpreting BCI’s

As explained earlier, BCI operates on a linear scale from 0 (not functional) to 100 (best). Since it is also similar to the percentage scale, it can be used as a form of performance percentage indicator of its ‘service potential’. Hence a “0” condition index translates to no potential for service and for “100”, full potential is available. It should be noted that the costs to improve

this potential is far from linear, as the costs to maintain a good bridge are lower than compared to improving a severely deteriorated bridge (ATKINS, 2002)

5.6.5 BCI Results

The BCI’s for each span for each bridge type is shown:

BCI's for each span for each bridge type

0

20

40

60

80

100

Bridge Type 1 Bridge Type 2 Bridge Type 3Bridges Stock

BC

I Val

ue

BCI 1st span(avg)BCI 1st span(crit)BCI 2nd span(avg)BCI 2nd span(crit)

Figure 5 5 Plot of BCI’s for each bridge span for each bridge type in the bridge stock

From the results, bridge type 1 gives a high percentage of ‘service potential’ and on the other extreme bridge type 3 has lost almost 50% of its service potential. Following a similar pattern to the BCS results obtained, the BCI (average) values for bridge type 1 differ greater with the corresponding BCI (critical) values for the same bridge type, than compared for bridge type 3. This is owing to the situation where bridge type 3 spans are close to reaching their threshold value and that maintenance work should be carried out as soon as possible in order not to risk the loss of important bridge elements.

5.6.6 Interpreting BSCI’s

The BSCI values (average and critical) serve as an aid for high-level strategic decisions that involve budget allocation for boroughs, councils and local authorities, which can be expected to be at the regional or national level. Funding to increase BSCI values are not linear, since it is more costly to improve conditions for a low BSCI score than a high BSCI score. Since the single BSCI output value computed does not give full information of the bridge stock, the BCI values that used a range of 0 to 100 can be used in conjunction with BSCI’s. The guidance for interpreting BSCI values (ATKINS, 2002) is as follows:

2010 87

Table 5 9 Interpretation of BSCI’s (ATKINS, 2002)

BSCI Range

BCS Range

Bridge Stock Condition based on BSCIAverage

Bridge Stock Condition based on BSCICritical

100 → 95 Very Good

1.0 → 1.3

Bridge stock is in a very good condition. Very few bridges may be in a moderate to severe condition.

Very few critical load-bearing elements may be in a moderate to severe condition. Represents very low risk to public safety.

94 → 85 Good 1.31 → 1.8

Bridge stock is in a good condition. A few bridges may be in a severe condition.

A few critical load-bearing elements may be in a severe condition. Represents a low risk to public safety.

84 → 65 Fair 1.81 → 2.7

Bridge stock is in a fair condition. Some bridges may be in a severe condition.

Potential for rapid decrease in condition if sufficient maintenance funding is not provided. Moderate backlog of maintenance work.

Wide variability of conditions for critical load bearing elements, some may be in a sever condition.

Some bridges may represent a moderate risk to public safety unless mitigation measures are in place.

64 → 40 Poor 2.71 → 3.7

Bridge stock is in a poor condition. A significant number of bridges may be in a severe condition.

Maintenance work historically under funded and there is a significant backlog of maintenance work.

A significant number of critical load bearing elements may be in a severe condition.

Some bridges may represent a significant risk to public safety unless mitigation measures are in place.

39 → 0 Very Poor 3.71 → 5.0

Bridge stock is in a very poor condition. Many bridges may be unserviceable or close to it.

Maintenance work is under funded with a huge backlog of work.

Many critical load-bearing elements may be unserviceable and are in a dangerous condition.

Some bridges may represent a high risk to public safety.

Figure 5 6 Histogram plot of BCI (average) values

BCI (average) Histogram

15

28 27

3

0

5

10

15

20

25

30

40-65 (poor) 65-85 (fair) 85-95 (good) 95-100 (very good)Range

No.

of b

ridge

spa

ns

The histogram above shows more detailed information than using the BSCI and hence the use of BCI (average) values is very useful. Each class range from ‘poor’ to ‘very good’ are not the same, as it is meant to reflect the different conditions of bridges. It can be seen that for a bridge stock of seventy-three bridges, fifteen are in the ‘poor’ category, which is almost 20% of the bridge stock by proportion that needs careful attention. The majority of the bridge stock is either ‘fair’ or ‘good’ which is a positive sign. Three bridge spans have been given the ‘very good’ classification. These may relatively new bridges that have been built, or bridges that are still at an early stage of their service life.

6. PREDICTING BRIDGE CONDITION WITH TIME

6.1 Introduction

In addition to being able to inspect bridges and assess their current state, good bridge management includes the ability to predict the bridge condition in the future. This will enable bridge engineers to deal with the uncertainties regarding the overall structural integrity of bridges, as well as be capable of coping with the changing conditions of bridges within the bridge stock. Being able to predict future bridge performance enables decisions to be made early regarding setting target performance levels, planning and scheduling of maintenance as well as budgeting for future expenditure. Preparedness is an important element of good bridge management and can be increased by using suitable prediction models.

6.2 Markov Chain Approach

The Markov Chain approach is a system applicable to bridge management due to its simplicity and properties. It can be employed to model the changes in a bridge condition over time. More importantly, it can be extended to model the make-up of a bridge stock over time. This approach is applicable when a discrete stochastic system possesses Markovian properties (RIMES, 1999).

These are:

1. The state space (i.e. possible values) are discrete;

2. The probability of X at t + 1 = k given the previous X0 = k0; X1 = k1, …Xt = j is equal to the probability of X at t + 1 = k given Xt = j.

This approach assumes ‘no memory’ and the probability of at the next time step is only dependent on the immediate previous time step. In bridge management, bridges are often assessed on a twenty-year horizon. By means of allocating the number of states the bridges can be in, the probability of bridges remaining in that state for each year can be computed. The initial state value is 1.0. The subsequent state values for each stage (twenty stages) are computed, by means of summing up the products of the stage transition probabilities with the stage number (weighting), explained below:





∑=

=N

iistatePStageValue

1)(

where for the current stage,

P(state)i = (1 - P(state)i-1 )previous stage * P(state)i + P(state)i * (P(state)i)previous stage

and N is the number of states used (i.e. three states)

6.2.1 Example Calculation

P(1) 0.99 Q(1) 0.01P(2) 0.95 Q(2) 0.05P(3) 1 Q(3) 0

Stage 0 Stage 1 Stage 2State 1 1 0.99 0.98State 2 0 0.01 0.02State 3 0 0 0

Stage value = 1.02

An example is shown above to illustrate the computation for stage 2 of a three-state model. In the above table, the probability of transition from state 1 to 2 is 0.99 (i.e. Q(1) = 0.01). The probability of transition from state 2 to 3 is 0.95 and Q(2) = 0.05. The computation for the shaded cell is:

0.01*0.99 + 0.95*0.01 = 0.02

And the stage value is the sum of the product of the stage probabilities and state values:

Stage value = 1*0.98 + 2*0.02 + 3*0 = 1.02

The limiting state is absorbing, such that bridges that have deteriorated in that state remain in that state (i.e. transition probability is 1) until maintenance is carried out. By plotting out the bridge condition with time over twenty years, the time which exceedence of the limiting state value occurs can be located. This aids bridge engineers by providing a means of probabilities of ‘failure’ as well as enabling the information to be translated to respective costs and knowing when to act accordingly.

6.3 Bridge Condition Example

It is proposed that the transition probabilities to be used are the Bridge Condition Index (BCI), which operates on

a linear scale of 0 (worst) to 100 (best). The degree of severity of bridges is linearly distributed over this range (i.e. BCI of 50 to 51 is the same as 90 to 91), except that costs are expected not to have a linear distribution. This is a useful approach as the BCI (average) is interpreted as ‘service potential’ and is used as a performance indicator.

Using the example for multi span bridges earlier on, the transition probabilities for a three-state Markov chain model with limiting stage value of 3 is proposed. The probabilities are in accordance to the BCI values for the ‘good’ bridge arranged in order of descending magnitude (i.e. P(1) = 0.9845 and P(2) = 0.9246). For the purposes of comparison, the other two bridges (‘medium’ and ‘bad’) are also modelled and the three are plotted together.

A summary of inputs is as follows:

Table 6 1 Input transition probabilities for bridge condition example

Bridge Condition P(1) P(2) P(3)Good 0.9845 0.9245 1.0

Medium 0.8859 0.8708 1.0Bad 0.5734 0.5369 1.0

Figure 6 1 Bridge condition with time

Bridge condition with time

0

1

2

3

4

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Years

Stat

e

goodmediumbadLimit

6.3.1 Bridge Condition Results

Observing how the stage changes with time over the twenty-year horizon, it is plain that for the bridge in ‘bad’ condition, the limiting stage is reached quickly after ten years. For both the ‘medium’ and ‘good’ bridges the maximum not exceeded and the ‘medium’ bridge is approaching the limit first. To take this example further, it is possible that the limiting stage value be defined as the BCS, thereby increasing the limiting stage value to 5 instead of 3. However, it should be noted that the BCS

2010 89

does not reflect the extent of severity linearly as BCI does and the additional inputs that need to be generated (i.e. new P(3), P(4)) either by more extensive bridge type modelling or judgment.

6.4 Bridge Aggregation Example

Due to the need for considering the changes in bridge condition over time for the entire bridge stock, it may be computationally exhaustive to carry out analyses using each bridge’s BCI value. A single output related to the BCI classifications can be used more easily for high-level strategic decision-making.

As such, after an evaluation of the distribution of bridge conditions in the bridge stock, bridges can be suitably aggregated into the 4 possible categories, depending on their BCI values (previously plotted using histograms). These are poor (40 to 65), fair (65 to 85), good (85 to 95) and very good (95 to 100). The transition probabilities may be assigned as the lower and upper limits of each category to generate the four-stage model. 100% for the ‘very good’ BCI classification cannot be used, as bridges will definitely not remain perfectly in the same condition in the next year, 99% is proposed as an alternative. Taking a step further, it is possible to aggregate bridges in to several different categories using other definitions of ranges to work with, depending on the bridge management needs.

The input probabilities are as follows:

Bridge Condition P(1) P(2) P(3)Very good 0.99 0.95 1.0

Good 0.95 0.85 1.0Fair 0.85 0.65 1.0Poor 0.65 0.40 1.0

Table 6 2 Input transition probabilities for bridge aggregation example

Using the classification similar to those for interpreting BCI values, the output is as follows:

Figure 6 2 Bridge condition with time using aggregation

Bridge condition with time

0

1

2

3

4

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Years

Stat

e

very goodgoodfairpoorLimit

6.4.1 Bridge Aggregation Results

It can be seen that the bridges in ‘poor’ condition deteriorate very quickly and plateau off to reach the limit after about ten years. The bridge in ‘very good’ condition deteriorates very slowly and the one in ‘fair’ condition just about reaches the limit after twenty years. For the one in ‘good’ condition, the condition is not too severe, however, maintenance would be necessary to prevent it from reaching the severe state. The deterioration of bridges is clearly non-linear; increasing at a decreasing rate and it is explained by the greater ease of bridges to deteriorate from a very good condition (e.g. brand new) to a poorer state. This has implications for the scheduling of maintenance and costs that are involved for the bridges in the bridge stock that come under any of the four categories. Taking this example further, the limiting stage value can be increased also to 5 instead of 3, similar to the BCS scale.

6.5 Bridge Stock Example

To further examine how the conditions of bridges in the bridge stock change with time, the proportion for each bridge condition in the bridge stock can be modelled. This can be achieved by using a deterioration model of the form:

State 0: p(1) q(1) 0

State 1: 0 p(2) q(2)

State 2: 0 0 1

In this three-state model, state 0 represents the best condition and 2 the worst condition, which is also the absorbing state. It is possible for states to move from





p(1) + q(1) = 1,

and p(2) + q(2)= 1

Only p(1) and p(2) need to be entered into the model to define the transition probability matrix.

Applying to a bridge stock model, p(1) represents the proportion of bridges in the bridge stock being in state 0 will remain in state 0 the following year. This is similarly applied for p(2). To analyze the different behaviour of how the bridge stock’s proportion of bridges are made up of over time, it is proposed that three types of bridge stock behaviour be used, for simplification, to be ‘good’, ‘fair’ and ‘poor’. The different scenarios with different probabilities are as follows:

Table 6 3 Input transition probabilities for bridge stock example

Bridge Stock Condition P(1) P(2)Good 0.95 0.90Fair 0.90 0.80Poor 0.80 0.70

The various bridge stock distribution plots showing the bridge stock profiles are as follows:

Figure 6 3 Distribution of bridge stock condition with time (good conditions)

Good Conditions

0

0.2

0.4

0.6

0.8

1

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Years

prob

abili

ty d

istri

butio

n

state 2state 1state 0

0 to 1, or 1 to 2, but not backwards. Bridges in state 2 remain in state 2, unless maintenance is carried out to improve the condition. p(1) is the probability that given a section is in state 0 now, it will remain in the same state the subsequent year. q(1) is the probability that given a section is in state 0 now, it will transit to state 2 the subsequent year.

Due to the constraint of total probability,

Figure 6 4 Distribution of bridge stock condition with time (fair conditions)

Fair Conditions

0

0.2

0.4

0.6

0.8

1

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Years

prob

abili

ty d

istri

butio

n


Figure 6 5 Distribution of bridge stock condition with time (bad conditions)

Poor Conditions

0

0.2

0.4

0.6

0.8

1

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

years

prob

abili

ty d

istri

butio

n


6.5.1 Bridge Stock Results

Bridge stocks subject to good conditions can represent maintenance being carried on time, to schedule, with sufficient allocation of funds. This is the opposite for bridge stocks subject to poor conditions. However, several other factors may come into play such as the location of bridges as a certain bridge stock may have a large proportion of its bridges being sited near the coast and be subject to severe corrosion by sea spray.

By observing the three types of condition plots, the bridge stock that is subject to good conditions have a higher proportion of bridges that still remain in the state 0 when compared to the bridge stock that is subject to poor conditions. In the good condition scenario, the proportion of bridges in the bridge stock that are in state 1 increases from zero over time. For both fair and poor conditions, the number of bridges in state 1increase and then begin to decrease when the years approach twenty. This is due to the bridges in the bridge stock deteriorating

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towards state 2. By the end of twenty years, less than half of the bridge stock is in state 2 for the good condition scenario and for fair conditions, it is slightly under 80%. For the poor conditions, a very high percentage (about 97%) of the bridge stock has deteriorated to the state 2 condition.

7. TRAFFIC COSTS

7.1 Introduction

In the UK, QUADRO (Queues And Delays at Roadworks) is a computer program used to determine the traffic delay costs due to maintenance activities and as such taken on board by the HA as a tool to aid the planning of roadworks. With respect to the management of bridges, examples of such works are resurfacing, general maintenance, waterproofing of bridge decks. QUADRO takes into account the costs borne by the user, in terms of value of time, cost of operating vehicles and costs due to accidents. Over time, several versions of QUADRO have been improved upon and integrated with cost benefit analysis packages which can extend computations for all elements of a project that need to be reviewed before obtaining approval (DETR, 1998)

QUADRO can be used to help engineers decide what optimal strategies are available to carry out roadworks in terms of scheduling of works and when they should be carried out. This is particularly useful for a series of works extending over varying locations. The program can also be used to evaluate proposed schemes and review the potential impacts of implementing them. Although QUADRO has been designed for use in rural areas, it can also be implemented in urban areas, with some limitations due to the complexity of junctions, configurations of diversions and invariably unpredictable elements of road user behaviour (DETR, 1998). Different types of information need to be available and input to the QUADRO program. They are:

1. Record of maintenance works carried out previously, with details on date, type and location of such activities. The cost of maintenance is also used. The duration and severity of traffic delay are taken into consideration.

2. The traffic history of areas are used, defined in standardized means such as the Annual Average Daily Traffic (AADT), proportion of Heavy Goods

Vehicles (HGV), with information on alternative routes, additional time for travel and distance to travel using these alternatives.

3. The history of imposed restrictions on load, height and width is also recorded.

The different types of bridge maintenance related costs and how they are quantified are mentioned below:

1. Costs due to traffic delays, quantified in terms of average delay time and value of time of the road user.

2. Costs due to traffic having to detour, using information provided concerning length of detour, value of time and additional travel time.

3. Accident costs and congestion costs, caused by reduction in traffic flow speed, also quantified using value of time of the road user and typical costs of accidents.

4. Environmental impact and social costs are relating to emissions of additional toxins and greenhouse gases caused by detouring traffic or congestion. These can be estimated using vehicle speeds and detour information.

7.2 TrafficCountExample

Several traffic counts were carried out in ten-minute intervals on 19th May 2004 on two bridges in the Central London area, namely Blackfriars bridge and Southwark bridge. Six counts were carried out in total for Blackfriars bridge and four for Southwark bridge. These bridges are located in a congestable area with different number of lanes carrying road traffic. The traffic counts included all road users, which were classified as cars, bicycles, HGV, motorcycles and buses. A vehicle was classified as a HGV if it was larger than a car in size and not a bus. This does not include small delivery vans as such.

The time of the traffic counts were carried out during the morning peak hours, although some modification are required for weekend and off peak hour behaviour. The costs borne by pedestrians also need to be assessed but time did not allow for a full pedestrian count to be carried out. The pedestrian count is therefore estimated to be 2000 per hour for Blackfriars bridge and 1500 per hour for Southwark bridge during peak hours.





7.2.1 Traffic Count Results

Using both northbound and southbound counts, an averaged traffic count and traffic composition are as follows:

Flow Blackfriars SouthwarkTotal 1504 477

Cars per hr 1005 306Bicycles per hr 218 72

HGV per hr 81 36MC per hr 165 51Bus per hr 35 12

Table 7 1 Flows from traffic count on Blackfriars & Southwark bridges

The make up of traffic for Blackfriars bridge for both directions are plotted as follows:

Figure 7 1 Traffic breakdown for Blackfriars bridge (northbound)

Blackfriars (Northbound)

0%

20%

40%

60%

80%

100%

0840-0855 0935-0945 0955-1005Time

Prop

ortio

n BusMotorcyclesHGVBicyclesCars

7 2 Traffic breakdown for Blackfriars bridge (southbound)

Blackfriars (Southbound)

0%

20%

40%

60%

80%

100%

0820-0830 0925-0935 0945-0955Time

Prop

ortio


It is observed that for Blackfriars bridge, there is a larger proportion of cars travelling in the southbound direction than the other way. Both car proportions for northbound and southbound are increasing with time up to about 10am in the morning. In the same manner, the proportion of buses noted were decreasing. The larger proportion of HGVs travelling earlier in the morning is observed and this is attributed to the fact that congestion builds up at morning hours and hence the need for earlier travel. Goods need to be unpacked, distributed and get ready for the next process of shipping or selling and hence extra time is required, compared to people travelling to work who need only to travel to the office from the carpark (less transit time).

The number of bicycles in the southbound direction is close to constant and for the northbound direction it seems to decrease as time approaches 10am. This is due to the cyclists getting to work earlier so that they have adequate time to change clothes, or freshen up before starting work. It is possible also that since congestion is peaking, their considerations for greater safety with less traffic affects their decision to travel earlier in the morning. It is believed that more employment avenues are located at the south of the river and hence a larger proportion of road users are made up of cars.

The following observations are made in the absence of a complete statistical survey of the bridges involved which is expected to take a disproportionate amount of time when compared to other analysis and discussions needed in this project. Hence, it has been accepted that although some slight inadequacies will invariably remain in this exercise, however, they are not severely misrepresented. The make up of traffic for Southwark bridge for both directions are plotted as follows:

Figure 7 3 Traffic breakdown for Southwark bridge (northbound)

Southwark (Northbound)

0%

20%

40%

60%

80%

100%

0855-0900 1040-1045Time

Prop

ortio


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Figure 7 4 Traffic breakdown for Southwark bridge (southbound)

Southwark (Southbound)

0%

20%

40%

60%

80%

100%

0900-0905 1035-1040

Time

Prop

ortio


For Southwark bridge, there is a similar pattern emerging for the northbound route, with the proportion of cars increasing as time goes by. The proportion of bicycles is large for the earlier hours of the morning. The proportion of HGVs seems to be unchanged. For the southbound direction, the number of cars decline for the second period of the traffic count. This can be attributed to the people commuting to work have reached their place of work since it is mid-morning. The HGV proportions are larger than the first period, which is explained by less congestion after the morning peak hour congestion allowing them to move more easily and hence, this time of travel is selected.

7.3 TrafficDelayCostExamples

Using the basic information above, costs of traffic delays have been computed using Excel to investigated the interaction between the various components of traffic and their contribution to traffic delay costs (e.g. traffic composition, length of detour, duration of delay).

The following steps have been carried out, using the traffic survey data collected and guidance notes (tables) that are in the Appendix:1. Input the annual averaged value of time per

vehicle for each vehicle type for 1998 scenario. This includes the different types of cars, such as ‘working’ car, or ‘non-working’ car, which relates to the purpose of travel as working or only to and from work respectively. When HGV information is unavailable, they are delegated values given for LGV instead. The working and non-working costs are allocated to all other vehicles except buses which are standardized.

2. A reduction percentage is used to adjust for decreased occupancy over time, due to increased income levels which allow people to purchase more cars. These values are 0.05% per year for working vehicles and 0.22% for non-working vehicles.

3. To adjust for increase in Gross Domestic Product (GDP), an indicator of wealth and hence related to value of time and annual rate of 3.28% has been applied up to 2002 then 2.19% up to 2004.

4. The split proportion between working and non-working vehicles needs to be entered. Since the traffic counts were carried out during a typical peak hour period, a larger percentage is used for working vehicles (e.g. 75% or 80% working vehicles, with the remaining as non-working).

5. Fuel costs are then computed, using appropriate cost coefficients.

The cost of fuel is estimated using the following function (DoT, 2003):

C = a + bV + cV2

Where

C = cost in pence per km of travel,

V = average speed in kilometers per hour,

a, b and c are parameters defined for each vehicle category.

The non-fuel resource cost are combined in a formula of the form (DoT, 2003):

C = a1 + b1/V

6. Estimates for the link speeds are used. It is estimated that cars travel at 40km/h, for HGV it is 30km/h, for bicycles it is 15km/h, for motorcycles it is 40km/h, for buses it is 35km/h and for pedestrians it is 8km/h. These speeds are for free flowing traffic.

7. Correction percentages for increased fuel cost are then used as well as fuel consumption corrections to account for increase in fuel and the growing efficiency of engines in prices over time. The fuel cost per kilometer for each vehicle type and purpose can then be obtained. It should be noted that fuel





is not needed for bicycles, but to consider the costs of the cyclist expanding energy (a form of cost to user), costs to bicycles are a proportion of that used for vehicles, but without adjustments made to increase in ‘fuel consumption’ and ‘costs’. The costs allocated to pedestrians have been selected to be a proportion of the costs borne by cyclists.

8. The detour distance (i.e. additional distance) experienced by road users is estimated to be 3.0km and additional time taken to travel this additional distance varies with each mode of travel. For full bridge closure and half bridge closure, the costs incurred will not be directly proportional since the responses to road user behaviour is expected to be highly non-linear, as well as the complex traffic interactions with alternative routes. The estimate reductions in speeds by a proportion of free flow speed is selected and then computed separately. The bridge length is estimated to be of the order of 0.35km and hence the two types of additional cost (full bridge closure and half bridge closure) to road users due to detour and delay can be computed.

9. To adjust for off peak travel behaviour and respective costs incurred, it is estimated that there daily, there are 4 peak hours, 8 off peak hours and 12 quiet hours (i.e. at night). The costs each road user experiences in the off peak and quiet hour period are expressed as a percentage of the peak hour, for simplification, taken as 50% and 30% of the peak hour costs respectively.

10. The costs of detour per day for both a full bridge closure and half bridge closure are then computed. This accounts for congestion due to reduced capacity of the bridge when 1 lane is not in service, as well as the congestion that builds up in alternative routes which road users choose to take.

7.3.1 Delay Costs Results (1st Example)

A summary of delay costs is as follows:

Table 7 2 Summary of delay costs from example

Cars HGV Bicycles Motorcycles Bus PedestriansCost per mode of travel, £ 2.20 4.05 2.05 5.39 37.70 0.68Additional delay & detour (full closure), £ 4.70 11.58 11.71 11.55 92.32 7.32

Additional delay & detour (1/2 closure), £ 4.03 9.92 10.03 9.90 79.13 6.27

Table 7 3 Cost of detour due to delay from example

Blackfriars SouthwarkCost of detour per day, £ 400 701.43 209 813.07

Cost of detour per day (half closure), £ 354 147.06 184 295.78

The proportional costs depending on each situation is shown below:

Costs for each bridge per hour

0

10000

20000

30000

40000

Blackfriars (fullclosure)

Blackfriars (1/2closure)

Southwark (fullclosure)

Southwark (1/2closure)

Situation

Cos

ts, £

TravelDetour cost

Figure 7 5 Breakdown of costs (per hour) for each bridge

The cost of detour per day for Southwark bridge is lower than that for Blackfriars bridge, due to the lower flows of traffic. The costs seem to be conservative in nature as both Blackfriars and Southwark bridge are very important bridges in the Central London area. It is likely that some uncaptured elements of traffic delay costs were not accounted for, such as a different set of costs borne by travellers if information concerning the delay was made available and their selected mode of travel changed. No attempt has been made to quantify the costs borne by other travellers in the vicinity (e.g. Underground, river boat service) due to the exponential increase of complexity in both data collection and computation. It is unknown how road users would change their mode of travel or how the available configurations of alternative routes will be utilised as well as the degree of utilisation.

It is recognised that a high degree of uncertainty inherently lies with the computation carried out. The classification of proportion of working and non-working vehicles, average link speed on the bridges, detour lengths and additional travel time, proportion of costs allocated to off peak and peak hours and quiet hours, all contributed to the uncertainties of the computation. However, with refinement, more traffic counts and better understanding of how the complex interactions of road user behaviour and congestion function, an improved estimate can be produced.

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To investigate a potential upper value of traffic delay costs, higher values have been used for the inputs. An increased percentage of working vehicles (85%), increased number of off peak hours (14 hours) and reduction of silent hours (6 hours) have been used.

7.3.2 Delay Costs Results (2nd Example)

A summary of delay costs is as follows:

Cars HGV Bicycles Motorcycles Bus PedestriansCost per mode of travel, £ 2.20 4.05 2.05 5.39 37.70 0.68Additional delay & detour

(full closure), £ 4.70 11.58 11.71 11.55 92.32 7.32

Additional delay & detour (1/2 closure), £ 4.03 9.92 10.03 9.90 79.13 6.27

Table 7 4 Summary of delay costs from example (2nd example)

Table 7 5 Cost of detour due to delay (2nd example)

Blackfriars SouthwarkCost of detour per day, £ 442 137.86 231 513.17

Cost of detour per day (half closure), £ 390 769.03 203 356.61

The costs have now increased by 10.3% as a result. More detailed statistical surveys of traffic flows, as well as the modelling of traffic behaviour under different delay circumstances can yield more accurate results, but these generated for this example satisfies the key design procedures and considerations.

7.4 Accident Cost Example

In addition to the costs incurred due to traffic delays, traffic accidents that occur on bridges can severely increase the costs incurred by road users. These are considered separately and can be used in combination to traffic delays. How these two complex mechanisms interact remains unknown and it is expected to require extensive computational analyses. For the purposes of understanding accident costs, a basic model has been developed.

The accident costs borne by road users are estimated using the following procedures:1. The accident rate per hundred million vehicle

kilometers is used, scaled to the volume of traffic from the traffic survey data; therefore the number of vehicle kilometers travelled per year is computed first.

2. The proportions of accidents are then allocated, to three categories, namely ‘fatal’, ‘serious’ and ‘slight’, in descending order of severity (1%, 14%, 85% respectively) using the prescribed guidance by the Department of Transport (2003). The number of casualties per accident is then assumed.

3. Using a percentage correction factor (2%) to adjust for the changes in cost over time, the average cost per casualty and per accident is used for the final computation of the total cost of accidents per year.

7.4.1 Accident Costs Results

A summary of accident costs is as follows:

Blackfriars SouthwarkFatal Serious Slight Fatal Serious Slight

Cost of accidents, £ 39 504.69 62 510.82 30 751.00 12 538.51 19 836.66 9976.99Total accident

cost per year, £132 766.50 42 152.16

Due to the simplified inputs of casualties per accident and proportion of fatal, serious and slight accidents for both Blackfriars and Southwark bridge, the proportional contributions of cost for each type of accidents are the same. In reality, this is expected to be different. Using the costs of accidents by type (adjusted for percentage changes each year), fatal accidents make up about 30% of the total costs, with serious injuries accounting for 47% and slight injuries 23%. This is different from the proportion of accidents allocated which are 1%, 14% and 85% respectively. This is difference is observed due to the high cost of fatal accidents which do not occur very often. Therefore in terms of costs, their contributions are larger overall, similarly for serious injuries that make up 14% of injuries, but accounts for 47% of the accident costs.

Figure 7 6 Breakdown of cost of accidents on Blackfriars bridge

Adjusted cost of accident(Blackfriars)

Fatal30%

Serious47%

Slight23%

FatalSeriousSlight





By means of another iteration using an increased percentage of working vehicles (85%) and increased number of off peak hours (14 hours) with a a reduction in silent hours (6 hours), a summary of accident results is as follows:

Blackfriars SouthwarkFatal Serious Slight Fatal Serious Slight

Cost of accidents, £

43 589.95 68 975.77 33 928.47 13 834.17 21 887.05 10 784.74

Total accident cost

per year, £146 494.20 46 505.96

Table 7 6 Cost of accidents for Blackfriars & Southwark bridges

There has been a 10.3% increase in costs of accidents for both bridges, with the types of accidents taking the same proportions as the earlier scenario due to the simplified inputs. The classification of peak hours and silent hours can by defined differently, as they can be defined accordingly to road users, residents in the vicinity, or to the local authority urban planning guidelines. The developed model can thus be used in to investigate further the impacts of these variables. It is expected that there are upper and lower bound values to these variables, such as the percentage of working vehicles not reaching a full 100% and the number of peak hours to be greater than one and less than twelve (i.e. half a day).

8. ENVIRONMENTAL IMPACT

8.1 Introduction

When traffic congestion builds up due to an accident delays or maintenance works, the amount of emissions from vehicles increase because of reduced vehicle speeds. These gases comprise of greenhouse gas carbon dioxide, particulates, lead compounds, hydrocarbons, compounds of nitrogen oxides and carbon monoxide.

8.2 Emissions Example

For Blackfriars bridge, three key emission gases are modelled in various scenarios to investigate the impacts on the environment, namely carbon monoxide (CO), nitrogen oxide compounds (NOx) and hydrocarbons (HC). The different scenarios investigated are made up of:

1. 100% free flow (no congestion)2. 80% free flow (little congestion)3. 60% free flow (moderate congestion)4. 40% free flow (severe congestion)5. 20% free flow (very severe congestion)

The following procedure is carried out according to DMRB (1998):

1. Input of vehicle speed, proportion of HGV, receptor distance.

2. Compute the number of cars and HGV. For simplification, buses and HGVs come under the same heading and bicycles are removed from the analysis. Motorcycles are included in the cars category for simplification since this is the less conservative than if they were to be classified as HGVs.

3. Speed correction factors for both cars and HGV are first applied, followed by year correction factors, to account for changes in emissions with speed and more efficient engines being developed over time respectively.

4. Correction factors for flows to take into account the number of vehicles; distance correction factors are then applied. The final outputs (per hour) are then obtained.

8.2.1 Emissions Results

A summary of emissions is as follows:

Units parts per million (ppm) parts per billion (ppb) parts per billion (ppb)% of free flow CO NOx HC

100 0.239 103.046 64.11980 0.314 118.211 79.70560 0.366 129.880 91.02140 0.523 171.760 132.16720 0.682 216.370 181.595

Table 8 1 Summary of emissions calculations

The UK’s National Air Quality Strategy (NAQS) objectives for reduced emissions for 2005 include reductions of NOx and HC emissions to not more than 150 parts per billion (ppb) and for CO emission it is 1.25 parts per million (ppm) (DMRB, 1998). The modelled results and superimposed NAQS limits are shown in the following plots:

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CO Emissions

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

020406080100

Flow conditions (% of free flow)

ppm

CO

UK NAQSObjective

Figure 8 1 Carbon Monoxide (CO) emissions for different flow conditions

Figure 8 2 Nitrogen Oxides (NOx) & Hydrocarbon emissions for different conditions

NOx & HC Emissions

0

50

100

150

200

250

020406080100Flow condition (% of free flow)

ppb

NOx

HC

UK NAQS Objective

The amount of CO emissions approximately triples when congestion becomes very severe, but does not indicate a likelihood of exceeding the NAQS limits.

However, the limits for NOx and HC emissions are exceeded when the flows deteriorate to approximately 50% of free flow conditions for NOx and 35% for HC respectively. In addition, the NOx emissions approximately doubles when congestion becomes very severe. For HC emissions this approximately triples. In order to ensure that the excessive emissions do not occur, it is essential that good maintenance procedures be followed and all reasonable steps are taken to disseminate information (e.g. early warnings of intentions to carry out maintenance work, use of appropriate diversion signs in the event of accidents) and manage traffic (e.g. good signage, well planned maintenance work).

9. DECISION-MAKING & PRIORITIZATION

9.1 Decision-making

9.1.1 Introduction

Upon the successful collection of bridge scoring data, modelling bridge conditions with time, estimating traffic delay and accident costs and environmental impacts, it is important to develop methods to aid decision-making to maximize the returns for investments into the various elements of bridge management. As such, understanding the breakdown of maintenance expenditure is necessary.

A brief summary of typical costs of maintenance work carried out on bridges is as follows:

Figure 9 1 Breakdown of maintenance costs for various countries (RIMES, 1999)





For the UK, the costs per bridge for the various maintenance activities are as follows:

Activity Cost, £

Inspection 1579

Routine maintenance 1579

Specialized maintenance 10 263

Repair & strengthening 9473

Mean maintenance cost per bridge 23 700

Table 9 1 Breakdown of costs for various maintenance activities (RIMES, 1999)

It is obvious that not all maintenance actions are required for all bridges, since older bridges need more repairs and strengthening and new bridges do not require them as much. However, the mean cost of maintenance per bridge is useful as a reference for allocating costs and alternatives. Comparing how decisions are made by other BMS, the BMS used in Denmark involves a prioritization programme (EC BRITE and EURAM, 1993), while Finland uses a repair index. Canada, which uses PONTIS, uses long term cost optimal strategies. France and Germany both rely on the judgment of engineers (Mahut B, 1998, Der Bundesminister fur Verkehr, 1992). Spain’s BMS evaluates the cost of repair as a proportion of replacement costs (Das P C, 1996). For the UK, cost benefit analysis is carried out for decision-making.

9.2 Prioritization

9.2.1 Introduction

In addition to decision-making, prioritization is an important element in bridge management. Denmark, Spain, Finland, USA (New York) and Canada uses optimal strategies within their BMS that imposes a minimum level of performance that is required. The influence of both cost and transport related policies are taken into account for the UK. Canada uses the lowest costs in the long term that avoids bridge failure. However, France, Germany, Norway, Slovenia and the UK do not use a prioritization technique when there is insufficient budget (Der Bundesminister fur Verkehr, 1982). This is practiced in Spain, Denmark, USA (New York) and Canada (Lauridsen J and Lassen B, 1998). Denmark and USA’s(New York) BMS’s both evaluate the consequence

of not implementing the optimal strategy in terms of economic cost. The criteria for prioritization are as follows:

Figure 9 2 Criteria for prioritization for various countries (Woodward R J et al, 1999)

9.2.2 Dynamic Programming

Dynamic programming can be used in bridge management to prioritize how resources are allocated to maintenance activities when given a set of possible alternatives in order to maximize the returns. This is a useful tool in determining long term strategies for bridge or element replacement and can take on board imposed restrictions in order to fulfill the requirements of bridge management.

The definitions of terms used are as follows:

1. Programming progresses in stages, which can be taken to mean the same as ‘steps’.

2. For each stage, the system is defined by the state it is in.

3. At each stage, a decision is made (e.g. amount of investment to be put in).

4. The state return function gives the results (consequential value) of the decision taken.

5. The optimal value of the system is then computed

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sequentially by stages, using a forward or backward pass. This then gives the optimal allocation of resources.

For a backward pass:

Stage k Stage k+1

Backward pass

Let the stage be called k

Then sk represents the amount of investment available at that time

The notation for decision to be taken is xk

The function is

fk*(sk) = Max {gk(sk,xk) + fk+1*{1)}

Where,

fk*(sk) is the optimal for the current stage k (denoted using the symbol ‘*’)

and fk+1*{sk+1) is the optimal for the ‘previous’ stage k+1

Also,

sk+1 = sk – xk

9.3 Budget Allocation Approach

In a bridge stock, the benefits derived for different bridges for the same maintenance activity vary, due to the current condition that bridges are in, as well as other benefits to road users and the community as a whole. For a simple model, a dynamic linear programme can be used to optimize the returns when different levels of investment are put in.

To define, for a backward pass method:

Stage: k

State: sk, represents the amount of investment available for the current and next bridge

Decision: xk

)}(*),({)(* 11 +++= kkkkkkk sfxsgMaxsfkx

sk+1 = sk – xk

Where,

fk*(sk) is optimal for current stage k and fk+1*{sk+1) is the optimal for next stage k+1.

In the table below, four maintenance activities are assumed. They represent varying degrees of maintenance, from less intensive (e.g. routine inspection) costing £25 000, to very intensive (£100 000). They can be either a single type of maintenance activity, such as road resurfacing, or a combination of activities, such as painting and bridge bearings replacement. It is proposed that four types of bridges in the bridge stock be used, similar to the four types of conditions defined by the BCI values. The benefits reaped from maintenance activities are assumed to increase with increasing investment levels and differ between the four different bridge conditions. This is due to the fact that bridges in the ‘very good’ condition will not yield a significant amount of benefits due to their present favourable state.

Comparing with a ‘poor’ or ‘fair’ bridge receiving substantial maintenance, the potential for greater benefits to be reaped is higher. Not all maintenance activities have the same effect for bridges and it is estimated in proportion to the investment levels used. It should be noted that the benefits are larger than each investment alternative as it is an assumed imposed condition that the benefits should outweigh the cost, otherwise the activity would not be beneficial at all and should not be considered altogether. For the purposes of this exercise, the sum of £100 000 budget is estimated to be available for investing and the tables of cost and benefits is as follows:

Table 9 2 Costs & benefits from budget allocation example

Maintenance Activity 1 2 3 4Investment, in £ 000 25 50 75 100

Poor 70 85 90 110Fair 45 75 85 130

Good 40 65 85 120Very good 40 70 80 100





Table 9 3 Budge allocation computation for example

Stage State Decision Benefit

4 25 25 40 *

50 50 70 *

75 75 80 *

100 100 100 *

3 25 0 40 *

25 40 *

50 0 70

25 40+40= 80 *

50 65

75 0 80

25 40+70= 110 *

50 65+40= 105

75 85

100 0 100

25 40+80= 120

50 65+70= 135 *

75 85+40= 125

100 120

2 25 0 40

25 45 *

50 0 80

25 45+40= 85 *

50 75

75 0 110

25 45+80= 125 *

50 75+40= 115

75 85

100 0 135

25 45+110= 155 *

50 75+80= 155 *

75 85+40= 125

100 130

1 100 0 155

25 70+125= 195 *

50 85+85= 170

75 90+45= 135

100 110

Note: The ‘*’ represents optimal value for the stage and state.

9.3.1 Budget Allocation Results (1st Example)

The optimal allocation is £25 000, £25 000, £25 000, £25 000 for each bridge. This is essentially equal distribution as the benefits gained by the bridges in ‘poor’ condition are not significant enough to warrant more investment.

Using a different assumed case, benefits gained from bridges in the ‘poor’ condition are higher:


Poor 50 85 100 150Fair 35 75 90 130

Good 35 75 85 120Very good 30 70 80 110

Table 9 4 Costs & benefits for 2nd example

The results obtained are as follows:

Stage State Decision Benefit

4 25 25 30 *

50 50 70 *

75 75 80 *

100 100 110 *

3 25 0 30

25 35 *

50 0 70

25 35+30= 65

50 75 *

75 0 80

25 35+70= 105 *

50 75+30= 105 *

75 85

100 0 110

25 35+80= 115

50 75+70= 145 *

75 85+30= 115

100 120

2 25 0 35 *

25 35 *

50 0 75 *

25 35+35= 70

50 75 *

75 0 105

25 35+75= 110 *

50 75+35= 110 *

75 90

100 0 145

25 35+105= 140

50 75+75= 150 *

75 90+35= 125

100 130

1 100 0 150

25 50+110= 160 *

50 85+75= 160 *

75 100+35= 135

100 150

Table 9 5 Budget allocation computation for 2nd example

9.3.2 Budget Allocation Results( 2nd Example)

The optimal allocations are: £50 000, 0, £50 000, 0; or £50 000, £50 000, £0, £0; or £25 000, £25 000, £50 000, 0; or £25 000, £50 000, £25 000, £0. There is a larger number of potential combinations for this model and more allocations are given to the bridges in the ‘poor’ condition because of the higher benefits reap from investment when compared to investing on bridges that are in ‘good’ condition.

A third assumed case uses even higher benefits gained from investing in bridges that are in poor condition:

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Table 9 6 Costs & benefits for 3rd example


Poor 50 90 120 160Fair 35 65 85 120

Good 35 60 85 115Very good 30 55 80 110

Table 9 7 Budge allocation computation for 3rd example

Stage State Decision Benefit4 25 25 30 *

50 50 55 *75 75 80 *100 100 110 *

3 25 0 3025 35 *

50 0 5525 35+30= 65 *50 60

75 0 8025 35+55= 90 *50 60+30= 90 *75 85

100 0 11025 35+80= 115 *50 60+55= 115 *75 85+30= 115 *100 115

2 25 0 35 *25 35 *

50 0 6525 35+35= 70 *50 65

75 0 9025 35+65= 100 *50 65+35= 100 *75 85

100 0 11525 35+90= 12550 65+65= 130 *75 85+35= 120100 120

1 100 0 13025 50+100= 15050 90+70= 160 *75 120+35= 155100 160 *

9.3.3 Budget Allocation Results ( 3rd Example)

This results in obtaining the optimal investment allocation of £50 000, £25 000, £25 000, £0; or £100 000, £0, £0, £0. For this third case, more investment is allocated for the bridges in ‘poor’ condition. Although allocating resources to bridges in the ‘poor’ condition optimizes the benefits to be derived, the developed model to predict future bridge conditions can be used to evaluate the consequence of not allocating resources to the other bridges.

10. IMPROVEMENTS TO BUDGET ALLOCATION APPROACH

10.1 Introduction to BCI optimization approach

Although budgets can be allocated optimally, as demonstrated earlier, it does not necessarily translate to optimal bridge conditions since the benefits derived for the different bridge conditions have been aggregated into a single value for optimization. The benefits derived can be made up of traffic time savings, aesthetics and environment benefits, which may not take into account the condition of the bridge itself. As such the previous approach involves budgets allocated to obtain the optimal aggregated benefits. In this suggested approach, a specific optimization target has been selected, namely the Bridge Condition Index (BCI). This approach seeks to allocate budgets to obtain the highest possible BCI.

The dynamic programme is defined as follows:

Maximize: ∏=

=3

1i ipz

Subject to ∑ ic < Budget

The stage is the maintenance activity number; the state is the funds available to carry out the maintenance actions; the decision is the type of maintenance actions carried out. For a backward pass, the recurrence equation is:

)}(*)({)(* 113,2,1 ++=×= iiinii sfnpMaxsf

Where

fi *(si) is optimal for current stage i and fi+1*{si+1) is the optimal for next stage i+1 And the transition equation is

)(1 ngss iii −=+





10.2 Service Potential (BCI) Examples

A simple model is set up to investigate how budget constraints influence the available service potential as well as the choice of maintenance actions to be taken. Three bridges are assumed in this model with service potential increasing with the number of maintenance actions taken. For simplification, three types of actions can be carried out and each bridge must undergo any one of them. The costs are assumed for each bridge based on their conditions and the three bridges used are defined to be declining in performance (i.e. bridge 3 is the oldest and bridge 1 to be the newest). Hence higher values are attributed to the newer bridge. For simplification, the BCI are divided by a hundred to convert them to probability first. They are then converted back to BCI.

Table 10 1 Inputs for service potential example

Maintenance Actions Bridge 1 Bridge 2 Bridge 31 0.85 0.7 0.62 0.9 0.8 0.753 0.95 0.9 0.85

The costs are also put in tabular form:

Table 10 2 Costs for service potential example

Costs, £ in thousands for each maintenance action Bridge 1 Bridge 2 Bridge 3

1 40 30 252 45 35 303 50 40 50

A simple linear programming model is created, working with an assumed budget of £120 000.

The programme is as follows:

Table 10 3 Computation for service potential exampleWhere

gi is the cost of the maintenance actions and pi is the available service potential (BCI).

Stage State Decision Total cost Available service potential

3 S3>50 3 50 0.85 *

30<S3<50 2 30 0.75 *

25<S3<30 1 25 0.6 *

2 S3>90 3 40 + 50 = 90 0.9*0.85 = 0.765 *

85 3 40 + 30 = 70 0.9*0.75 = 0.675

2 35 + 50 = 85 0.8*0.85 = 0.68 *

1 30 + 50 = 80 0.7*0.85 = 0.595

80 3 40 + 30 = 70 0.9*0.75 = 0.675 *

2 35 + 30 = 65 0.8*0.75 = 0.6

1 30 + 50 = 80 0.7*0.85 = 0.595

70 3 40 + 30 = 70 0.9*0.75 = 0.675 *

2 35 + 30 = 65 0.8*0.75 = 0.6

1 30 + 30 = 60 0.7*0.75 = 0.525

65 3 40 + 25 = 65 0.9*0.6 = 0.54

2 35 + 30 = 65 0.8*0.75 = 0.6 *

1 30 + 30 = 60 0.7*0.75 = 0.525

60 2 35 + 25 = 60 0.8*0.6 = 0.48

1 30 + 30 = 60 0.7*0.75 = 0.525 *

55 1 30 + 25 = 55 0.7*0.6 = 0.42 *

1 120 3 50 + 70 = 120 0.95*0.675 = 0.64125 *

2 45 + 70 = 115 0.9*0.675 = 0.6075

1 40 + 70 = 110 0.85*0.675 = 0.57375

10.2.1 BCI Optimization Results (1st Example)

The optimal BCI of the system is 64.125, with the decisions of 3, 3, 2. All £120 000 is spent. It is expected that budget has a strong influence on decisions made for maintenance actions, therefore further steps are taken to investigate this.

Two additional scenarios are assumed, one for having an increased budget by 10% to £132 000 and another having a reduced budget by 10% to £108 000. Their respective results in BCI values and decisions are computed below:

Table 10 4 Computation for service potential example (increased budget)


3 S3>50 3 50 0.85 *

30<S3<50 2 30 0.75 *

25<S3<30 1 25 0.6 *

2 S3>90 3 40 + 50 = 90 0.9*0.85 = 0.765 *

85 3 40 + 30 = 70 0.9*0.75 = 0.675

2 35 + 50 = 85 0.8*0.85 = 0.68 *

1 30 + 50 = 80 0.7*0.85 = 0.595

80 3 40 + 30 = 70 0.9*0.75 = 0.675 *

2 35 + 30 = 65 0.8*0.75 = 0.6

1 30 + 50 = 80 0.7*0.85 = 0.595

70 3 40 + 30 = 70 0.9*0.75 = 0.675 *

2 35 + 30 = 65 0.8*0.75 = 0.6

1 30 + 30 = 60 0.7*0.75 = 0.525

65 3 40 + 25 = 65 0.9*0.6 = 0.54

2 35 + 30 = 65 0.8*0.75 = 0.6 *

1 30 + 30 = 60 0.7*0.75 = 0.525

60 2 35 + 25 = 60 0.8*0.6 = 0.48

1 30 + 30 = 60 0.7*0.75 = 0.525 *

55 1 30 + 25 = 55 0.7*0.6 = 0.42 *

1 132 3 50 + 70 = 120 0.95*0.675 = 0.64125

2 45 + 85 = 130 0.9*0.68 = 0.612

1 40 + 90 = 130 0.85*0.765 = 0.65025 *

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10.2.2 BCI Optimization Results (2nd Example)

For a budget of £132 000, the optimal decision is 1, 3, 3 with optimal BCI of 65.025. There is a remainder of £2 000 left over.

Table 10 5 Computation for service potential example (decreased budget)


3 S3>50 3 50 0.85 *

30<S3<50 2 30 0.75 *

25<S3<30 1 25 0.6 *

2 S3>90 3 40 + 50 = 90 0.9*0.85 = 0.765 *

85 3 40 + 30 = 70 0.9*0.75 = 0.675

2 35 + 50 = 85 0.8*0.85 = 0.68 *

1 30 + 50 = 80 0.7*0.85 = 0.595

80 3 40 + 30 = 70 0.9*0.75 = 0.675 *

2 35 + 30 = 65 0.8*0.75 = 0.6

1 30 + 50 = 80 0.7*0.85 = 0.595

70 3 40 + 30 = 70 0.9*0.75 = 0.675 *

2 35 + 30 = 65 0.8*0.75 = 0.6

1 30 + 30 = 60 0.7*0.75 = 0.525

65 3 40 + 25 = 65 0.9*0.6 = 0.54

2 35 + 30 = 65 0.8*0.75 = 0.6 *

1 30 + 30 = 60 0.7*0.75 = 0.525

60 2 35 + 25 = 60 0.8*0.6 = 0.48

1 30 + 30 = 60 0.7*0.75 = 0.525 *

55 1 30 + 25 = 55 0.7*0.6 = 0.42 *

1 108 3 50 + 55 = 105 0.95*0.42 0.399

2 45 + 60 = 105 0.9*0.48 0.432

1 40 + 65 = 105 0.85*0.6 0.51 *

10.2.3 BCI Optimization Results (3rd Example)

Using a reduced budget of £108 000, the optimal decision is 1, 2, 2 with a BCI of 51. There is a remainder of £3 000 left over.

A summary of results is as follows:

Table 10 6 Results for different budget allocations

Case Budget % change in budget BCI % change of original BCI1 £120 000 - 64.1 -2 £132 000 + 10.0 65.0 + 1.403 £108 000 - 10.0 51.0 - 20.47

Figure 10 1 Changes in BCI with investment

Changes in BCI with investment

50

55

60

65

70

100 105 110 115 120 125 130 135Investment, £000

BC

IBy comparing the outcomes in terms of BCI for the

three different budgets, it is observed that when the budget is increased or decreased by about 10.0% (£132 000 or £108 000), the changes in BCI by proportion of the original are +1.40% and –20.47% respectively. This non-proportional behaviour reflects the increased amount of budget required to increase the BCI and a sharp decline in BCI if budgets were to be reduced.

10.3 Maintenance Costs Examples

Applying weightings to each bridge by means of using assumed maintenance costs of £50 per square meter for the deck areas for the three bridges used in the earlier model for traffic costs calculations, the cost of full maintenance (option 3) can be obtained. They are then scaled down to account for the two other maintenance options. The inputs are as follows:

Table 10 7 Costs & bridge deck areas for new service potential example

Deck area, m2 Cost, £ in thousandsBridge 1 1200 60Bridge 2 700 35Bridge 3 1000 50

Table 10 8 Inputs for new service potential example

Maintenance Actions Bridge 1 Bridge 2 Bridge 31 0.92 0.87 0.532 0.95 0.88 0.553 0.98 0.89 0.57

The costs are also put in tabular form:

Table 10 9 Costs for new service potential example

Costs, £ in thousands for each maintenance action Bridge 1 Bridge 2 Bridge 3

1 40 30 302 50 32 403 60 35 50

A simple linear programming model is created, working with an assumed budget of £120 000. The upper and lower limits of the available BCI that has been converted to probabilities are obtained from the BCI (average) model and the upper limit of maintenance costs have been obtained above. The mid-values are proposed to be approximately the median value for the probabilities and for the costs the other values are estimated. Both





costs and probabilities decrease in bridge maintenance actions, with 1 being the most basic maintenance activity and 3 being the most extensive activity.

The outputs are obtained:

Table 10 10 Computation for 4th service potential example


3 S3>50 3 50 0.53 *

40<S3<50 2 40 0.55 *

30<S3<40 1 30 0.57 *

2 S3>85 3 35+50=85 0.89*0.57= 0.5073

75 3 35+40=75 0.89*0.55= 0.4895 *

2 32+40=72 0.88*0.55= 0.484

1 30+40=70 0.87*0.55= 0.4785

65 3 35+30=65 0.89*0.53= 0.4717 *

2 32+30=62 0.4664

1 30+30=60 0.4611

62 2 32+30=62 0.4664 *

1 30+30=60 0.4611

60 1 60 0.4611 *

1 120 3 60+60=120 0.452 *

2 50+65=115 0.448

1 40+75=115 0.45

10.3.1 BCI Optimization Results (4th Example)

The optimal decision is 3, 1, 3 with costs of £120 000 (no remainders left over). The optimal BCI is 45.2.

Increasing the budget by 10% to £132 000, the following results were obtained:

Table 10 11 Computation for 5th service potential example (increased budget)


3 S3>50 3 50 0.53 *

40<S3<50 2 40 0.55 *

30<S3<40 1 30 0.57 *

2 S3>85 3 35+50=85 0.89*0.57= 0.5073 *

75 3 35+40=75 0.89*0.55= 0.4895 *

2 32+40=72 0.88*0.55= 0.484

1 30+40=70 0.87*0.55= 0.4785

65 3 35+30=65 0.89*0.53= 0.4717 *

2 32+30=62 0.4664

1 30+30=60 0.4611

62 2 32+30=62 0.4664 *

1 30+30=60 0.4611

60 1 60 0.4611 *

1 132 3 60+65=125 0.98*0.4717= 0.462

2 50+75=125 0.95*0.4895= 0.465

1 40+85=125 0.92*0.5073= 0.467 *


The optimal decision is 1, 3, 3 with costs of £125 000. The optimal BCI is 46.7. There is a remainder of £7 000.

Investigating a decrease in budget available, a 10% decrease (i.e. £108 000) was introduced with the following results:

Table 10 12 Computation for 6th service potential example (decreased budget)


3 S3>50 3 50 0.53 *

40<S3<50 2 40 0.55 *

30<S3<40 1 30 0.57 *

2 S3>85 3 35+50=85 0.89*0.57= 0.5073 *

75 3 35+40=75 0.89*0.55= 0.4895 *

2 32+40=72 0.88*0.55= 0.484

1 30+40=70 0.87*0.55= 0.4785

65 3 35+30=65 0.89*0.53= 0.4717 *

2 32+30=62 0.4664

1 30+30=60 0.4611

62 2 32+30=62 0.4664 *

1 30+30=60 0.4611

60 1 60 0.4611 *

1 108 3 Not possible

2 Not possible

1 40+65=105 0.92*0.4717= 0.434 *


The optimal decision is 1, 3, 1 with costs of £105 000. The optimal BCI is 43.4. There is a remainder of £3 000.

A summary of results is as follows:

Table 10 13 Results for different budget allocations (Examples 4 to 6)

Case Budget % change in budget BCI % change of original BCI1 £120 000 - 45.2 -2 £132 000 + 10.0 46.7 + 3.3 3 £108 000 - 10.0 43.4 - 4.0

Changes in BCI with investment

43

44

45

46

47

100 105 110 115 120 125 130Investment, £000

BC

I

Figure 10 2 Changes in BCI with investment (using maintenance costs)

2010 105

For the reduced budget scenario, constraints to the expenditure have been imposed due to lack of resources. It is observed that the percentage change in BCI available is greater when funds are reduced than if they were to be increased. The first two cases have optimal strategies that involve utilizing maintenance option 3 for bridge 3 which represents sound judgment as investment should be put into the most severe BCI contributor. However, with a reduced budget, only maintenance activity 1 can be selected.

In real terms, only an additional £5 000 is necessary to increase the BCI by 3.3% since there has been a saving of £7 000 for the £132 000 budget case. For a reduced budget, £105 000 has been spent (remainder of £3 000). It is possible that the £3 000 or £7 000, whichever scenario applies, be channelled towards the bridge that has the low BCI value to further improve the overall system.

Comparing percentage change for actual money spent with the base value of £120 000 scenario, for £5 000 more (4.2%), the BCI increases by 3.3%. For an expenditure of £105 000 (12.5% less), the BCI decreases by 4%. In this manner, the costs to increase the BCI in terms of value are higher (4.2% budget increase for 3.3% BCI increase) and for a significant reduction of budget, the BCI falls by a smaller proportion. This indicates that a reduced budget does not severely affect the BCI value, but a low value of 43.4 can be considered as ‘poor’. The poor ratings of the 3rd bridge have dominated the final output value. However, as mentioned before, the ‘savings’ can be re-channelled back to areas that need investment and the remainder of £3 000 is a third of the reduced expenditure (£120 000 reduced to £105 000), which is significant. This translates to a potential increase of the eventual optimal BCI.

11. CONCLUSION

This project seeks to provide the reader an understanding into the key bridge management concepts and principles practiced in the UK. For a broader perspective, bridge management systems elsewhere in the world, such as countries in continental Europe and the USA have been discussed as well. Learning the latest methods practiced in the UK for inspecting and assessing bridges, reviewing maintenance needs and prioritization, several different models have been developed to assist in good bridge management.

After scoring systems have been developed to assess the condition of bridges and extending them to the bridge stock, Markov chain models have been used to model changes in condition with time at both the individual bridge level as well as bridge stock level. Using different traffic conditions, delay cost models have been developed to derive estimates of both delay costs as well as accident costs. An emission model has also been created to estimate the increased impacts on the environment due to reductions in speed.

Finally, this project utilizes dynamic programming to accomplish optimal budget allocation and optimizing bridge conditions given a specific budget allowance. These two approaches have different aims and as such, their procedures yield different results. The use of such approaches is expected be proven as useful tools to both the engineer as well as transport policy makers.

It has been demonstrated that the limiting bridge conditions may be reached prematurely due severe deterioration of a key bridge element. This has implications for emergency unplanned bridge maintenance, which could potentially disrupt traffic and consequentially increase road user costs significantly. Much attention is required for the input values for modelling bridge conditions over time and diligent monitoring and adhering to inspection schedules that have been set out can achieve this.

The costs borne by road users are dependent many variables and more extensive studies can be made to establish upper and lower bounds of the derived estimates. Assigning ‘costs’ to the increased emissions was anticipated to be both difficult and subjective owing to the assumptions required for their assignment. Therefore it has not been attempted, however this could be a potential area for more extensive research. The examples of optimizing budget allocation and bridge condition can be extended over to a bridge stock, or several bridge stocks and this requires the specific decisions taken by the bridge engineer. From the examples in the project, it has been shown that the available service potential of bridges can potentially fall disproportionately due to a decrease in budget.

With the use of the developed models to estimate various road user costs, optimize bridge conditions, optimize budget allocation, the systems approach to bridge management discussed in this project is expected to benefit both technical and non-technical





users. The tools can aid higher-level strategic decision-making individuals who seek to understand the different interacting mechanisms of traffic flows, costs and different policies, as well as potentially use the modelling techniques to champion their proposals for improving the current local environment or region. Simple computational techniques can aid the engineer to establish a sensitivity to the environment impacts of decisions made in bridge management, as well as be more capable of tackling social and political issues that can involve planning policies and regeneration projects.

12. FUTURE RESEARCH

To take this project further, there are several possible research topics that can be considered. Structural assessments and reliability models can be developed using established methods and these can be integrated into the prioritization techniques or used together with developed models to better improve bridge condition modelling with time. The assumptions on the available budget for computation examples have been based on a limited number of factors. As such, more detailed study into typical budget allocations for each county, council or local authority could be undertaken in order to investigate the different results derived from ‘local’, optimized solutions and nationwide (i.e. ‘global’), optimized solutions. Additional prioritization techniques can be considered, such as using event tree analysis, probability and statistical theories to deal with other uncertainties as well as regression approaches. The composition of the bridge stocks can also be modelled using appropriate distributions and sufficiently sized sample sizes.

With regards to the environmental impacts of bridge maintenance, noise level modelling can be carried out to supplement the emissions models that have already been developed. Further detailed study on the road user choice behaviour can be taken and integrated with government policies encouraging the use of public transport (e.g. trains, buses). It should be noted that good bridge management not only includes ensuring the bridge stock is well managed in terms of structural integrity and budget management, but the environmental, social and political issues need to be addressed as they play important roles in each engineering process.

REFERENCES

Andersen, N H (1990) DANBRO – A Bridge Management System for many levels. In: Bridge Evaluation, Repair and Rehabilitation, edited by Andrzej Nowak, Kluwer Acadamic

ATKINS (2002) CSS Bridge Condition Indicators Volume 3: Evaluation of Condition Indicators

ATKINS (2002) CSS Bridge Condition Indicators Volume 2: Bridge Inspection Reporting

Baker, D (1999) The maintenace of highway structures. In: Management of highway structures, ICE, Thomas Telford

Bourn, John (2003) Maintaining England’s Motorways and Trunk Roads. National Audit Office

Bourn, John (2001) The National Road Maintenance Condition Survey. National Audit Office

Bourn, John (1996) Highways Agency: The Bridge Programme. National Audit Office

Bourn, John (1995) Expenditure on Motorways and Trunk roads. National Audit Office

Bourn, John (1991) Management of Road Maintenance. National Audit Office

Bourn, John (1989) Backlog of Maintenance of Motorways and Trunk Roads. National Audit Office

Bourn, John (1988) Regulation of Heavy Lorries. National Audit Office

Branco, F A and Brito, J (1996) Bridge management from design to maintenance. In: Recent Advances in Bridge Engineering, CIMNE Barcelona 1996, edited by J. R. Casas, F.W. Klaiber and A. R. Marí, 1996

2010 107

Calgaro, J A (1997) Maintenance et Reparation des Ponts. Presse de l’ENPC Znidaric A, Moses F (1997) Structural safety of existing road bridges. In: 7th ICOSSAR conference, Kyoto, 1997

Clausen, P (1992) Introduction to Bridge Management Systems. In: 14th Congress New Delhi 1992, IABSE

Das, P C (1998) New Developments in Bridge Management Methodology. In: Structural Engineering International, Volume 8, Number 4, November 1998

Das, P C (1996) Bridge Management Methodologies. In: Recent Advances in Bridge Engineering, CIMNE Barcelona 1996, edited by J.R. Casas, F. W. Klaiber and A. R. Marí, 1996

Department of Transport (2003) Economic Assessment of Road Schemes. The COBA Manual Volume 13 Section 1

Der Bundesminister fur Verkehr (1992) Richtlinie zur Tragfahigkeitseinstufung bestehender Strassenbrucken der neuen Bundeslander in Lastklassen nach DIN 1972. Ausg. Dez. 1985

Der Bundesminister fur Verkehr (1982), Shaden an Brucken and anderer Ingenierurbauwerken, Dokumentation 1982

EC BRITE and EURAM (1993) Assessment of Performance and Optimal Strategies for Inspection and Maintenance of Concrete Structures Using Reliability Based Expert Systems. In: Report P3091, Aalborg, Denmark, 1993

HA (2003) SMIS, A guide for structure inspectors

HA (2003) SMIS, A guide for structure engineers

HA (1998) DMRB Volume 11 Section3

Hawk, H and Small E P (1998) The BRIDGIT Bridge Management System. In: Structural Engineering International, Volume 8, Number 4, November 1998

Lauridsen, Jørn and Lassen, Bjørn (1998) The Danish Bridge Management System (DANBRO). In: The Management of Highway Structures, The Institution of Civil Engineers and Highway Agency, London, 22-23 June 1998

Mahut, Brigitte (1998) Maintenance of the Bridges on the French national network; Classification of the condition of structures. Bridge Management in Europe (BRIME) Workpackage 1, March 1998. Transport Research Laboratory, Crowthorne

Naumann, J (1998) Development of a Bridge Management System in Germany. In: Operation and Maintenance of Large Infrastructure Projects, Balkema, Rotterdam 1998, edited by Vincentsen & Jensen

OECD Report (1992) Bridge Management

Road Directorate, Denmark Ministry of Transport (1995) Bridge Maintenance on the Danish main road network

Road Infrastructure Maintenance Evaluation Study (1999) Network level Management model (Pavement and Structure management system) for Markov chains. RIMES, Workpackage 3 1999

Söderqvist, M K and Veijola, M (1998) The Finnish Bridge Management System. In: Structural Engineering International, Volume 8, Number 4, November 1998

Stensvold, Børre (1997) Computer Aided Bridge Management in Norway. In: International Conference Repair of Concrete Structures from Theory to Practice in a MarineEnvironment, 545-553, Svolvær, Norway, May 1997





The Department of the Environment, Transport and the Regions (1998) Getting the best roads for our money, An introduction to QUADRO

The Highways Agency, BA 63/94 (1994) Inspection of Highway Structures, Part 5

Thoft-Christensen, P (1996) Bridge Management Systems. present and future. In: Recent Advances in Bridge Engineering, CIMNE Barcelona 1996, edited by J. R. Casas, F. W. Klaiber and A. R. Marí, 1996

Thompson, P D, Small, E P, Johnson M and Marshall, A R (1998) The Pontis Bridge Management System. In: Structural Engineering International, Volume 8, Number 4, November 1998

Vorshrift 169/89 (1989) Brucken im Verkehrsbau; Nachrechnung von Strassenbrucken. Beton und Mauerwerk

Woodward, R J, Kaschner R, Cremona C and Cullington D (1999) Review of current procedures for assessing load carrying capacity. Bridge Management in Europe (BRIME) Deliverable D1. Transport Research Laboratory, Crowthorne

Woodward R J, Bevc L, Perus I, Mahut B and Grefstad K (1999) Review of existing procedures for optimisation. Bridge Management in Europe (BRIME) Deliverable D3. Transport Research Laboratory, Crowthorne

Woodward, R J, Godart B and Vassie P R (1999) Review of existing BMS and definition of inputs for the proposed BMS. Bridge Management in Europe (BRIME) Deliverable D4. Transport Research Laboratory, Crowthorne

Woodward R J, Asudillo Pastor R, Arreieta Torrealba J M, Velando Cabanas C and Lozano Bruna C (1999) Decision on repair and replacement. Bridge Management in Europe (BRIME) Deliverable D7. Transport Research Laboratory, Crowthorne

Yanev, B S (1998) Bridge Management for New York City. In: Structural Engineering International, Volume 8, Number 3, August 1998.

Znidaric J and Moses F (1995) Assessment of safety and remaining service life of existing bridges. ZAG Ljubljana

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Bridge Management A System Approach for Decision … · 2010 67 Bridge Management a System Approach for Decision Making Reginald W. Stratt (PhD) Postdoctoral student at the Department