04-01-01_PAV_GPH_aplicación-en-Filipinas-HDM-4

Implementing A Pavement Management System For The Philippines

Morosiuk, G

TRL Limited, UK

Pradhan, N High Point Rendel, UK

and Jimenez, N

Department of Public Works and Highways, Philippines SYNOPSIS The Department of Public Works and Highways (DPWH) in the Philippines is responsible for more than 26,000 km of national roads which consist of asphaltic concrete, cement concrete and unsealed roads. Under an ADB-funded project, a Pavement Management System (PMS) is being developed for the DPWH which uses the Highway Development and Management model, HDM-4, as the economic analytical tool to develop both long term (strategic) plans and medium term multi-year works programmes for the management of this road network. Information on road inventory, surface condition, traffic volumes and compositions, etc, is stored by the DPWH in a customised database named Confirm. One of the first tasks of this project was to develop a process for the aggregation and analysis of the key parameters to derive homogeneous lengths of road required for HDM-4 analysis. HDM-4 requires information on more parameters than are normally available in a road database. Therefore HDM-4 was configured to the Philippines conditions to establish a Network State Matrix, with each Network State having appropriate representative values. The homogeneous lengths of road were exported to HDM-4 through a customised HDM-4 Interface between the Confirm database and the HDM-4 analytical tool. The representative values were assigned to the parameters with missing values in the HDM-4 Interface. It was also necessary to calibrate the road deterioration and road user effects models to ensure that future predictions made by HDM-4 reflected local observations. At the time of this study, the deterioration models in HDM-4 for cement concrete pavements were ‘absolute’ rather than incremental. These absolute models predict the condition of a concrete pavement given the condition of the road when new. As HDM-4 predicts the future deterioration of roads for a specified analysis period, these concrete models do not lend themselves for analysis of a concrete road network that contains sections with various construction years. This study examined the use of the HDM-4 incremental deterioration models for pavements with stabilised bases as alternative models for predicting the performance of cement concrete pavements. This paper describes the process of creating and adapting a PMS for the Philippines national road network. INTRODUCTION The Department of Public Works and Highways (DPWH) in the Philippines is responsible for the management and maintenance of more than 26,000 km of national roads. There is considerable competition for relatively scarce resources to meet the growing demands for road maintenance and rehabilitation of this road network. Therefore the DPWH took the decision to upgrade some of their current systems and procedures to ensure that expenditure priorities are established by rational methods which maximise benefits from expenditures on the road network. Under an ADB-funded project, a Pavement Management System (PMS) has been developed for the DPWH which uses the Highway Development and Management model, HDM-4, as the economic analytical tool to

6th International Conference on Managing Pavements (2004)

TRB Committee AFD10 on Pavement Management Systems is providing the information contained herein for use by individual practitioners in state and local transportation agencies, researchers in academic institutions, and other members of the transportation research community. The information in this paper was taken directly from the submission of the author(s).

develop both long term (strategic) plans and medium term multi-year works programmes for the management of this road network. This paper describes the process of creating and adapting a PMS which supports needs analysis, multi-year programming and annual budgeting for the preservation of road pavements on the national road network in the Philippines, customised to local conditions. PAVEMENT MANAGEMENT PROCESS The Philippine national road network comprises pavements with asphalt and cement concrete surfacings as well as lengths of road that are unsealed. These sections of road are of varying condition and age and therefore the challenge is to identify an appropriate strategy for maintaining them over the long term. To do so requires an understanding of the deterioration behaviour of the various types of pavement and forecasting the impact of this deterioration on road users. The process therefore begins with defining the road asset in terms of pavement and geometric composition and collating data that relates to asset deterioration. Together these form the core of information on which forecasts can be made of deterioration. Long Term Planning The first stage in the process comprises the long term aspects of planning which involves setting out the long term goals and objectives for the road network, taking into account forecasts of growth in the various input parameters. Long term planning involves the analysis of a road network as a whole, typically requiring the preparation of medium to long term, or strategic, estimates of expenditure for road maintenance and development under various budget and economic scenarios. Predictions may be made of road network conditions under a variety of funding levels together with forecasts of required expenditure under defined budget heads. The physical road system is usually characterised by lengths of road, or percentages of the network, in various categories defined by parameters such as surface type, traffic flow and physical condition. The results of the planning exercise are of most interest to senior policy makers in the road sector, both professional and political. Medium Term Planning The second stage involves preparation of the medium term programme which provides a more accurate assessment of the road network needs over a planning period of say five or six years. This level of planning involves the preparation, under budget constraints, of multi-year road works and expenditure programmes in which those sections of the network likely to require maintenance, improvement or new construction are identified. The physical road network is considered at the programming stage on a section-by-section basis, with each section being considered as homogeneous in terms of physical attributes. The programming activity produces estimates of expenditure in each year, under defined budget heads, for different types of road works and for each road section. Budgets are typically constrained, and a key aspect of programming is to prioritise the road works in order to find the best use of the constrained budget. The typical application of this is the preparation of budgets for the rolling multi-year works programme for a road network, or sub-network. Annual Planning The third stage is the annual infrastructure programme comprising specific, well defined projects derived from the medium term plan. These projects are validated in terms of their feasibility and are prioritised before inclusion in the annual programme. For this short-term planning, road schemes are packaged for implementation. At this stage, designs are refined and prepared in more detail; bills of quantities and detailed costing are made, together with work instructions and contracts. Detailed specifications and costing are likely to be drawn up, and detailed cost-benefit analyses may be carried out to confirm the feasibility of the final scheme. Typical preparation activities are the detailed design of an overlay scheme; the detailed design of major works such as a junction or alignment improvement, lane addition, etc.



Programme Preparation How these programmes are prepared is an important component of the pavement management process. For each plan there is a need to apply the concepts of asset management and utilise the tools of the Pavement Management System to ensure that projects which provide the greatest economic benefit are considered for inclusion in each programme. The tools are therefore used to guide the development of these plans. However it is important to note that they are not an end in themselves, but rather provide a contribution to the planning process. Pavement Management Systems do not account for desired goals outside the framework of asset management. That is, national goals may include the need to address calamities or social needs in preference to economic criteria. Hence, these extraneous factors need to be addressed during the final preparation of these plans. Basic Components of the PMS Modern pavement management systems utilise information such as road condition, inventory and traffic data to predict the future performance of road networks for planning and programming purposes. This type of information collected by the DPWH is being stored in a customised database named Confirm. In addition to containing network referencing information, inventory definition, traffic and condition data for the road network, the database also includes a variety of analysis tools and comprehensive facilities for the spatial, graphical, schematic and tabular presentation of data in the system and results from analyses undertaken in the database. The PMS developed under this project extends the facilities of the Confirm database to enable further analysis of the data, including transformation of data into the units and format required for HDM-4 analyses. HDM-4 then provides the tool for the economic analysis of pavement maintenance and improvement policies using data from Confirm. The PMS uses the HDM-4 applications of strategy and programme analyses. Strategy analysis is used to derive the long term plan whilst programme analysis is used to derive the multi year works programme. CONFIGURATION OF THE HDM-4 MODEL HDM-4 is an economic road investment tool which has been developed to assist engineers and planners with road investment planning. Basically HDM-4 predicts the rates of deterioration of roads under alternative maintenance strategies and the road user costs for each alternative. It then conducts an economic analysis on the various alternatives and provides the user with economic indicators to assist with road investment planning. In order for HDM-4 to predict appropriate rates of road deterioration and road user costs, it is necessary firstly to configure the software and then calibrate the individual models. The data in HDM-4 that needs to be configured to local conditions can be categorized under the following headings:

Road Network Vehicle Fleet Road Works Traffic

Road Network In the HDM-4 Configuration folder, representative values for various parameters were stored that reflect typical conditions of the Philippine national road network. These representative values are used for deriving the long term plan through the application of the HDM-4 strategy analysis. Road network data used for long term planning consists of: • Traffic Volume • Road Class • Geometry Class • Construction Quality • Structural Adequacy • Ride Quality • Surface Condition • Surface Texture • Climate



For example, the ride quality in HDM-4 is described in terms of the roughness of the road surface in International Roughness Index (IRI) units. In the Configuration folder in HDM-4, four categories of roughness were defined; namely Good, Fair, Poor and Bad. The representative roughness values for these four categories for each of the three surface types (i.e. asphalt, concrete and gravel) are given in Table 1. Associated with each representative value is a range of roughness values. These ranges are used to assign individual road sections with appropriate data values for HDM-4 analysis as described later in this paper.

Table 1 Representative Roughness Values and Ranges

Asphalt Concrete Gravel Roughness

Category Range Representative Value Range Representative

Value Range Representative Value

Good ≤ 3.0 2.5 ≤ 4.0 3.5 ≤ 6.0 5.0

Fair 3.1 – 5.0 4.0 4.1 – 6.0 5.0 6.1 – 9.0 7.5 Poor 5.1 – 7.0 6.0 6.1 – 8.0 7.0 9.1 - 12.0 10.0 Bad > 7.0 8.0 > 8.0 9.0 > 12.0 14.0

Vehicle Fleet It is necessary to describe the vehicles that operate on the road network. In HDM-4 this is done by selecting and defining representative vehicle types for the main vehicle classes. The vehicle classification adopted by the project consists of 12 vehicle types, ranging from a motor-tricycle to large trucks with more than 5 axles. For each representative vehicle, the required inputs for HDM-4 are divided into two groups, namely basic vehicle characteristics and economic unit costs. HDM-4 contains default values for the vehicle characteristics of 16 motorised vehicle types. The default values for the 12 selected vehicle classes were amended to appropriately describe the Philippine vehicle fleet. HDM-4 requires the economic unit costs of vehicle components such as new vehicle price, tyres, fuel, oil, maintenance labour, etc. In trying to estimate these unit costs, particularly new vehicle prices and cost of tyres, it is important to take into account the spectrum of vehicle models within each vehicle class. Economic unit costs were derived for the 12 representative vehicle classes from surveys of vehicle distributors, tyre manufacturers, vehicle fleet operators, etc. To determine economic costs, it was necessary to deduct import duties, excise taxes and value added tax. Road Works In creating maintenance or improvement standards in HDM-4, the user needs to define the works activities associated with each standard. In defining a works activity, it is necessary to specify the cost of that activity. HDM-4 provides the facility to define default costs for each activity, which are then automatically assigned to that activity when creating a maintenance or improvement standard. These costs can be specified in both economic and financial terms. Traffic When conducting HDM-4 analyses, it is necessary to specify the traffic composition and the traffic growth rates for each length of road that is to be analysed. For strategy analysis where representative values are used, ‘national’ traffic composition and growth rates were used. The traffic composition was based on the 12 vehicle classes selected to represent the Philippine vehicle fleet. The traffic growth rate was derived for four 6-year blocks covering a period of 24 years, and specified separately for private, public and freight transport. DEFINING THE ROAD NETWORK It is necessary to define the road network in the appropriate manner for a given HDM-4 analysis. The following sections describe the stages in processing the road information data stored in the Confirm database to define the road network in an appropriate manner for HDM-4 analysis.



Aggregation of Road Characteristics For HDM-4 analysis of a road network, data are required by HDM-4 for lengths of road that are homogeneous for selected characteristics of the pavement. Data stored in the Confirm database are generally for short lengths of road (e.g. roughness at 100 metre intervals, surface condition at 200 metre intervals). These data need to be aggregated into longer lengths that are suitable for network level analysis and that are more appropriate for representing practical maintenance treatments. Each homogeneous length of road is referred to as a Planning Road Section (PRS). The aggregation and homogenisation is carried out in the Confirm database prior to exporting PRS’s of the national road network to HDM-4 for either strategy or programme analysis. Rules in the database create the PRS’s by aggregating data and creating lengths that are homogenous in all the selected criteria. Where short homogeneous lengths are created, a further aggregation process creates lengths that are sufficiently representative to be used in the analyses. The first stage of aggregation involves joining together lengths of pavement with similar values of the same characteristics (e.g. traffic, condition, pavement width) to create an aggregated length for each characteristic. Rules for aggregating sections are created for each characteristic independently since, at this stage, the aggregation of one characteristic has no effect on the aggregation of another. For example, the rules for traffic may be: AADT ≤ 750 751 < AADT ≤ 2500 2501 < AADT ≤ 8000 8001 < AADT ≤ 20000 20001 < AADT Adjacent lengths of road with traffic flows in the same band would be considered as homogeneous in terms of traffic. The value attached to the aggregated length is then derived from a rule for that characteristic. The rule can be created to use the minimum, maximum, sum, average or length weighted average value from the values making up the aggregated length. For the characteristic surface type, no aggregation is possible and for others there may be limiting values for which aggregation can take place at all (e.g. rutting less than zero and greater than 100 mm will not be allocated to a state). On receipt of data for a sample of the national road network, a list of the characteristics of the sections that are required for aggregation are created together with the acceptable ranges for each class of each characteristic. Lengths with no information on any of these characteristics are not aggregated into a longer length (e.g. a length with no traffic flow will not be considered for aggregation with other lengths), nor allowed to continue further with the HDM-4 analysis. Other characteristics may or may not be present for a length but these do not affect the aggregation with adjacent lengths (e.g. if the thickness of a pavement layer is missing, this will not prevent aggregation with an adjacent length). The aggregation process considered the following characteristics: • Surface Type (no aggregation across different surface types) • Traffic Flow • Speed Flow (number of lanes and single or dual carriageway) • Surface Age • Roughness • Surface Condition Derivation of Homogeneous Lengths From the aggregated lengths, homogeneous lengths are then created. In the aggregated lengths, each characteristic has the same value but the start and end positions of the aggregated lengths for all the characteristics will not be aligned. Homogeneous lengths are created by ‘chopping’ the aggregated lengths for the different characteristics to create aligned lengths in which all characteristics are homogenous.



An example is shown in Figure 1, illustrating four characteristics. In this example, the surface type is Asphalt between 0m & 5,700m and Concrete between 5,700m & 12,500m. Over this 12.5 km length of road, there are changes in AADT, type of carriageway and roughness at the chainages illustrated in Figure 1. This 12.5 km length of road is then ‘chopped’ up into lengths so that all the characteristics are homogeneous over each length. For example, the first 1.2 km of road (i.e. H1) is a single carriageway with an asphalt surfacing carrying 2250 vehicle per day and has a roughness of 4.0 IRI. Although the next 1.55 km has the same surface type, traffic and roughness, the road has changed from being a single carriageway to a dual carriageway, resulting in a new homogeneous length (i.e. H2) being created. A continuation of this process results in nine homogeneous lengths being created for this 12.5 km example length of road. Distance (m) 0 1200 2750 4500 5700 7250 8000 10000 11000 12500

Surface Type Asphalt Concrete

AADT (veh/day) 2250 4500 1500

Carriageway single dual single

Roughness (IRI) 4.0 5.5 7.0 4.5 6.0 3.5

Homogeneous Lengths H 1 H 2 H 3 H 4 H 5 H 6 H 7 H 8 H 9

Figure 1 Creation of homogeneous lengths from aggregated lengths

In creating the homogenous lengths some lengths may be shorter than the minimum length considered appropriate for HDM-4 network level analysis, although HDM-4 does not itself have a minimum length. Where possible, lengths less than the minimum length are joined to an adjacent length until the new length is longer than the minimum length or until no further amalgamation was possible. The characteristic of surface type is the only characteristic that cannot be amalgamated. Lengths shorter than the minimum length are not exported to HDM-4 but are identified in the exception report on the export process. When lengths are amalgamated, where possible the values of numerical characteristics for the amalgamated length (e.g. traffic flow) are determined by the same rule that is used to produce the initial aggregated length for that characteristic (i.e. minimum, maximum, sum, average or length weighted average). Where the characteristic takes a numerical value but a single value cannot be sensibly determined from interpolating the values from the adjacent lengths (e.g. number of lanes), the value of the longer length is used for the amalgamated length. The new homogeneous, amalgamated lengths are the Planning Road Sections that are transferred to HDM-4 for analysis. Network States In deriving the long term plan (i.e. for HDM-4 strategy analysis) the Philippine national road network was sub-divided into categories, resulting in a multi-dimensional Network State Matrix (NSM). Each cell of this matrix is referred to as a Network State. The categories for strategy analysis were: a) Surface Type (3) - Asphalt, Concrete, Gravel b) Traffic Volume (5) - Very High, High, Medium, Low, Very Low c) Roughness (4) - Good, Fair, Poor, Bad d) Surface Condition (4) - Good, Fair, Poor, Bad e) Speed Flow (3+1) - 2-lane 2-directions, 2-lane 1-direction, 4-lane 2-directions (Asphalt & Concrete only) - Narrow 2-lane 2-directions (Gravel)



This equates to 240 (5 x 4 x 4 x 3) possible Network States for asphalt pavements, 240 for concrete pavements and 80 (5 x 4 x 4) for unsealed roads, giving a total of 560 Network States for the national road network. Representative values were assigned to the categories, as illustrated for the five traffic categories in Table 2. In this example, the representative traffic volume for asphalt surfaced roads carrying ‘Low’ volumes of traffic was 1500 vehicles per day. For the HDM-4 strategy analysis, all asphalt surfaced roads carrying between 751 and 2500 vehicles per day were assigned this representative traffic volume of 1500 vehicles per day.

Table 2 Traffic categories

Asphalt Concrete Gravel Traffic

Category Range Representative Value Range Representative

Value Range Representative Value

Very Low ≤ 750 500 ≤ 750 500 ≤ 100 50

Low 751 – 2500 1500 751 – 2500 1500 101 – 200 150 Medium 2501 - 8000 5000 2501 - 8000 5000 201 – 500 350 High 8001 – 20,000 15,000 8001 – 20,000 15,000 501 – 1000 750 Very High > 20,000 25,000 > 20,000 25,000 > 1000 1250

As described earlier, the Philippine national road network data stored in the Confirm database is aggregated to derive homogeneous PRS’s. Each PRS is then assigned to a Network State. In the database, rules interpret the characteristics of each PRS to identify the cell of the NSM to which the PRS is allocated. Any value of the PRS characteristic in the range associated with the representative value of the Network State cell is sufficient to allocate a PRS to that cell. The total length of the PRS’s in each Network State is treated as a homogeneous length of road for the purposes of strategy analysis in deriving the long term plan. The representative values of each cell were assigned as the parameter values of these homogeneous lengths of road for use in strategy analysis. Some cells of the NSM involve more than one characteristic. It is therefore likely that a value for one of the characteristics may satisfy one cell while the value of another characteristic satisfies another cell. The allocation rules in the Confirm database create a unique cell for the PRS. For example, the surface condition of an asphalt road may be described as being ‘Good’ when cracking is < 1% and ‘Fair’ if the cracking is between 1% and 3%. Similarly ‘Good’ in terms of rutting may mean that rutting is < 3mm and ‘Fair’ if rutting is between 3 and 6mm. If a PRS has zero cracking and 5mm of rutting then the PRS is classified as ‘Good’ in terms of cracking and ‘Fair’ in terms of rutting. The allocation rules identify how to interpret the characteristics of the cell to enable the allocation to proceed. The rules are hierarchical and the allocation proceeds until the rules are broken. In the above example, if cracking is assigned a higher status than rutting, then the PRS will be allocated to the ‘Good’ surface condition cell even though the rutting characteristic is ‘Fair’. Identifying PRS and Assigning Data HDM-4 strategy analysis is used to derive the long term plan, whilst HDM-4 programme analysis is used to derive the multi year programme. For strategy analysis, each Network State is analysed as a homogeneous length of road, with the representative values assigned to each cell of the NSM used as the parameter values in the analysis. For programme analysis, the individual PRS’s are analysed using the measured values of the parameters for each PRS rather than the representative values. In HDM-4 there are 159 analysis parameters that may be assigned to a road section, although some parameters are not applicable to all pavement types. HDM-4 requires the data to be loaded in specific formats and supplied in particular units of measurement. The data held in the Confirm database are not necessarily in the same units



as those required in HDM-4. The necessary transformations were incorporated into the interface between HDM-4 and the database. Not all the data required by HDM-4 for analysis are available in the Confirm database. A set of default values for each analysis parameter was prepared for each Network State and kept in the HDM-4 workspace. The NSM was then imported to the HDM-4 Interface. Relevant maintenance and improvement standards, and traffic composition and growth rates associated with each Network State were also imported to the HDM-4 Interface. As stated earlier, the PRS’s are assigned to specific cells of the NSM. The default values from the cell were assigned to the appropriate PRS for the parameter values not populated directly from the Confirm database. This ensured that all the parameters were populated with appropriate data for each PRS to enable an HDM-4 analysis to be carried out on the road section. In addition to the section parameters associated with each PRS, HDM-4 uses parameters for other aspects of the analysis (e.g. parameters associated with each vehicle type, maintenance cost parameters, economic parameters). Configuring and updating these values is undertaken in HDM-4. HDM-4 ANALYSIS HDM-4 analysis is carried out for various purposes related to the planning and programming process for network preservation and development. Based on the objectives of the analysis, the process of data preparation may differ. The various stages in this process include:

Determine Scope of Analysis Define Analysis Parameters Specify Long Term Plan Parameters Specify Multi Year Programme Parameters Undertake HDM-4 Analysis

In the first stage, the scope of the analysis needs to be established. The types of issues that need to be addressed are: • Is the objective of the analysis to derive a long tem plan or a multi-year works programme? • What network is to be analysed? – All national roads; specific Regions or Districts, etc? Once the scope of the analysis has been established, the general analysis parameters need to be specified. This includes parameters such as the start of the analysis period, the length of the analysis period, the discount rate, the unit of currency for the analysis, etc. The road network and the vehicle fleet also need to be identified at this stage. The user can select specific sections of road and specific vehicle types for the analysis (i.e. it is not necessary to use all the road sections defined in the road network and all the vehicles types defined in the vehicle fleet). Strategy Level Analysis The steps in strategy analysis are defined as follows: • Categorise road network into matrix cells (Network States) • Define representative parameter values for each cell • Define maintenance and/or improvement standards for each cell • Specify budget constraints • Model pavement deterioration for each matrix cell • Calculate road user benefits • Select maintenance/improvement standards which optimise user benefits The initial analysis assumes an unconstrained budget to produce the unconstrained programme. The effect of various budget constraints can then be investigated to produce a series of constrained programmes for various budget levels. Programme Level Analysis The steps in carrying out Programme Analysis can be defined as follows: • Select candidate road projects



• Determine maintenance and/or improvement options • Specify budget limits and periods • Produce prioritised list of projects for the budget period As for strategy analysis, an unconstrained works programme is initially produced. Budget constraints are then applied to determine constrained works programmes. HDM-4 Outputs HDM-4 is used as an analytical tool to economically justify various scenarios and works programmes. It is important that the outputs of the runs of HDM-4 analyses are managed properly. The process to manage HDM-4 outputs includes: • Exporting HDM-4 generated reports to the Confirm database • Exporting prioritised Works Programme • Store and protect the HDM-4 Workspace that has been used to derive the final long term or medium tem

plans. On completion of an HDM-4 analysis, data and parameters used in the analysis, together with results from the analysis are imported to the Confirm database for viewing and presentation using the database facilities. The HDM-4 Interface ensures that data and results received from HDM-4 are in a format suitable for loading into the database and for subsequent reporting and presentation purposes. When data and results from an HDM-4 analysis are loaded into the database, the values remain in the units used in the HDM-4 analysis and are not converted into the units originally used in the database. To ensure consistency of data and for archiving purposes, it is not possible to amend any of the values in the database. Each set of data parameters and results are identified by the Study Description and Date/Time of the analysis in HDM-4. The HDM-4 Workspace can be used again for further analysis in HDM-4, but when outputs are saved again in the database, they have a different Study Description, Date/Time identifier. MODELLING THE PERFORMANCE OF CONCRETE PAVEMENTS At the time of this study the deterioration models for concrete pavements in HDM-4 were ‘absolute’ rather than incremental. Absolute models predict the condition at a point in time given the condition of the road when new. Incremental models predict the change over a period of time, say a year, given the condition at the start of the year. Incremental models are used to predict the rate of deterioration of bituminous and unsealed roads in HDM-4. The main type of concrete pavement in the Philippines is a jointed plain concrete pavement (JPCP). The distresses modelled in HDM-4 for a JPCP are joint faulting, joint spalling, cracking and roughness. It is not unreasonable to estimate the condition of a new JPCP in the Philippines in terms of these distresses. It can be assumed that faulting, spalling and cracking are zero for a new JPCP, and that roughness is say 3.5 IRI. Knowing the current age and condition of a JPCP section, it is relatively straightforward to use the ‘new’ condition values and calibrate the deterioration trends so that the current condition of the JPCP section is predicted by HDM-4. The analysis period can then be extended to predict the future deterioration of the JPCP section. Although the ‘absolute’ concrete models can be used in HDM-4 to predict the deterioration of individual JPCP sections in this manner, it is much more difficult to use this technique when analysing many concrete sections with various construction years all at the same time. This could possibly be achieved by deriving calibration factors for individual sections based on their year of construction and current condition. However, as this was not considered as a realistic and sustainable approach, the option of using the HDM-4 incremental models for stabilised bases to model the deterioration of JPCP sections was explored. Rates of Deterioration of Concrete Pavements In the NSM five levels of traffic have been specified for concrete pavements as listed previously in Table 2. Also four road conditions have been specified in the NSM as Good, Fair, Poor and Bad. For JPCP in the Philippines, the conditions are listed in Table 3. This gives 20 Network States (5-traffic x 4-condition) for concrete



pavements. As these 20 Network States cover the full spectrum of traffic and road condition encountered in the Philippines, the rates deterioration of these 20 JPCP sections were investigated.

Table 3 Road condition categories for concrete pavements

Road Condition Category

Roughness(IRI)

Faulting (mm)

Spalling (% of joints)

Cracking (% of slabs)

Good 4.0 1 0 1 Fair 5.0 2 1 5 Poor 7.0 4 5 20 Bad 9.0 6 10 50

As stated earlier, the deterioration of concrete pavements needs to be modelled from the year of construction in HDM-4. Therefore the periods of time taken to reach the conditions of Good, Fair, Poor and Bad from ‘New’ need to be estimated for each of the five traffic levels. Also the traffic levels when the sections were originally ‘New’ need to be estimated based on the current traffic levels. The estimates of the age of each Network State and the appropriate traffic levels when they were ‘New’ are given in Table 4.

Table 4 Age of Network States and AADT at Construction

VL = 500 L = 1500 M = 5000 H = 15000 VH = 25000

Age AADT Age AADT Age AADT Age AADT Age AADT Good 6 420 5 1295 4 4445 3 13725 3 22880 Fair 15 320 12 1050 10 3720 8 11845 7 20325 Poor 35 180 30 620 25 2390 20 8305 15 16050 Bad 40 155 35 535 30 2060 25 7165 20 13840

The roughness of the concrete sections at construction was estimated to be 3.5 IRI, with no other defects present. Using these parameter values as the condition of a ‘New’ JPCP section, the rates of deterioration were modelled for the periods of time given in Table 4 with the appropriate AADTs and traffic growth rates. These rates of deterioration were then adjusted to match the current conditions of Good, Fair, Poor and Bad (as given in Table 3). The future deterioration of each Network State was then modelled for a further 25 years from the current condition. Rates of Deterioration of Stabilised Base Models In HDM-4, incremental models exist for predicting the deterioration of stabilised bases, with bituminous surfacings. The pavement type STSB (surface dressing on a stabilised base) was examined to see if the models for this pavement type could be used to predict the deterioration trends of concrete pavements. The thickness and resilient modulus of the stabilised bases were set to those of the concrete slabs. The thickness of the bituminous surfacing (single surface dressing) was set to a minimal value of 10mm. Stabilised base sections were created with a current condition of Good, Fair, Poor or Bad, with their ages as listed in Table 4 for the 20 Network States and with their current AADT. The amount of cracking for STSB sections is expressed as per cent of carriageway area that is cracked rather than per cent of slabs that are cracked which is used for JPCP sections. Spalling is not modelled for STSB pavements, therefore potholes in STSB were used as a proxy for spalling in JPCP. The transformations of cracking and spalling in JPCP to cracking and potholing units for STSB sections that were classified as Good, Fair, Poor and Bad are shown in Table 5.



Table 5 Transformation of Distresses

Cracking Spalling - Potholing JPCP

% of cracked slabs STSB

% area cracked JPCP

% of spalled joints STSB

no of potholes/km Good 1 0 0.1 1 Fair 5 0.5 1 20 Poor 20 5 5 220 Bad 50 20 10 660

NB A standard sized pothole in HDM-4 has an area of 0.1m2. The transformation of cracking was based on the assumption that the number of transverse cracks per cracked slab was 1 crack per slab when 5% of slabs are cracked; 2 cracks per slab when 20% of slabs are cracked and 4 cracks per slab when 50% of slabs are cracked. The length of slabs are typically 4.5 metres and a crack in HDM-4 is assumed to have an influence of 0.5 metres across the crack. Therefore, for example, 4 transverse cracks on a slab affect 2 meters of the 4.5 metres of a slab. This equates to approximately 40% of the area of the slab. If 50% of the slabs are cracked then the total area of carriageway that is cracked is 20%. The transformation from spalling to potholing was based on the assumption that the length of spalling at each spalled joint when 1% of joints are spalled was 1 metre, 2 metres when 5% of joints are spalled and 3 metres when 10% of joints are spalled. The width of spalling was assumed to be 0.5 metres (same as cracking). This gives an area of 0.5m2, 1m2 and 1.5m2 at each spalled joint for 1%, 5% and 10% spalled joints respectively. A standard size pothole in HDM-4 has an area of 0.1m2,which equates to 5, 10 and 15 standard sized potholes for the three spalled areas. There are 222 slabs in a lane-kilometre – i.e. 444 slabs on a kilometre length of 2-lane road. Therefore if 10% of the joints are spalled on a 2-lane road, then 44 joints are assumed to each have 3m of spalling, i.e. 15 standard sized potholes. This equates to a total of 600 potholes/km. Comparison of Concrete and Stabilised Models In HDM-4, cracking is assumed to have initiated when the cracked area of the carriageway is > 0.5%. The values in Table 5 show that 0.5% of the cracked carriageway is equivalent to 5% of cracked slabs. The time from construction to reach 5% of cracked slabs was determined for JPCP sections for the five traffic levels. Similarly the crack initiation times for STSB sections were derived for the five traffic levels. These crack initiation times were calibrated so that they matched the times predicted for JPCP sections. The calibration factors derived for the STSB sections ranged between 0.85 for the low trafficked sections and 1.2 for the heavily trafficked sections. The rates of deterioration of the STSB sections were then adjusted to predict the roughness, cracking and spalling values as predicted by the JPCP sections after 25 years. In HDM-4 there are two calibration factors for roughness, Kgm and Kgp. The value of the calibration factor for the environment (Kgm) was kept constant at a value of 0.2. The value of Kgp (roughness progression) was then altered to adjust the roughness progressions. For sections that are in ‘’Poor’ and ‘Bad’ condition at the start of the analysis period, it is extremely unlikely that they would be allowed to continue to deteriorate for a further 25 years without some minimum amount of repair. For this reason the upper limit of potholing was set to 2000/km after 25 years. The derived adjustment factors for roughness, potholing and cracking are listed in Table 6.



Table 6 STSB Adjustment Factors

Very Low Low Medium High Very High

Roughness 0.15 0.15 0.15 0.35 0.3 Potholing 1.5 0.6 0.19 0.063 0.038 Good

Cracking 0.05 0.06 0.07 0.082 0.077

Roughness 0.25 0.2 0.19 0.12 0.2 Potholing 0.88 1.41 0.145 0.057 0.037 Fair

Cracking 0.06 0.07 0.075 0.081 0.086

Roughness 0.35 0.18 0.11 0.1 0.16 Potholing 0.55 0.23 0.076 0.024 0.017 Poor

Cracking 0.06 0.058 0.057 0.06 0.066

Roughness 0.15 0.17 0.2 0.2 0.24 Potholing 0.31 0.124 0.041 0.014 0.0083 Bad

Cracking 0.038 0.037 0.033 0.033 0.035 The rates of cracking, potholing and roughness progression for STSB pavements using these adjustment factors were compared with the rates of deterioration for the JPCP Network States. The comparison of rates of roughness progression for STSB and JPCP sections carrying for medium levels of traffic (i.e. AADT of 5000) are illustrated in Figure 2. These plots clearly indicate that the incremental models for stabilised bases can be used to predict the performance of concrete pavements.

Roughness - Medium Traffic

4

6

8

10

12

14

16

-40 -30 -20 -10 0 10 20 30

Years

Rou

ghne

ss

Figure 2

Roughness progressions for JPCP and STSB sections with Medium Traffic

Calibration of the STSB Models Having illustrated that the STSB models can be used for modelling the deterioration of concrete pavements, it was necessary to calibrate the STSB models to predict the performance of the JPCP as observed in the Philippines. Firstly the JPCP models were calibrated to reflect the rate of deterioration of concrete pavements as observed in the Philippines for individual JPCP sections. The derived JPCP calibration factors were defined as a

JPCP - Poor

JPCP - Fair

JPCP - Good

STSB - Fair

STSB - Poor STSB - Bad

JPCP - Bad

STSB - Good



product of the appropriate adjustment factor for JPCP pavements used to predict the Good, Fair, Poor and Bad conditions for the five traffic levels and a multiplicative factor (MF). As the adjustment factors applied to the JPCP models produce the same rates of deterioration as the STSB models with the adjustment factors given in Table 6, then the MFs can be applied to the STSB adjustment factors to produce the same rates of deterioration as the calibrated JPCP models. Calibration factors for STSB pavements were derived by applying these MF values to the STSB adjustment factors. At a strategic analysis level, representative values are used in HDM-4. Therefore representative calibration factors were derived for STSB pavements for each of the five traffic levels. These representative calibration factors are given in Table 7. These representative calibration factors have been assigned to STSB pavements in the Network State Matrix to model the deterioration of JPCP in the Philippines.

Table 7 Representative STSB Calibration Factors

VL L M H VH Cracking Initiation Kcia, Kciw 0.85 0.85 0.85 1.0 1.2 Cracking Progression Kcpa, Kcpw 0.13 0.135 0.14 0.15 0.16 Potholing Progression Kpp 0.4 0.2 0.065 0.025 0.015 Roughness Environment Kgm 0.2 0.2 0.2 0.2 0.2 Roughness Progression Kgp 0.6 0.35 0.3 0.25 0.25

SUMMARY A Pavement Management System has been developed that enables the DPWH to manage and maintain the Philippine national road network comprising cement concrete, asphalt concrete and unsealed roads. The main components of the PMS consist of a database which stores road condition, inventory and traffic information, HDM-4 as the economic analytical tool, and an HDM-4 Interface linking the database with HDM-4. A method of producing homogeneous lengths of road, referred to as Planning Road Sections (PRS), has been developed under this project. The HDM-4 Interface has been designed to populate each PRS with appropriate parameter values for any parameters that are not stored in the database but are required for HDM-4 analysis. This was achieved by creating a Network State Matrix which covers the range of conditions encountered on the Philippine national road network, and then assigning each PRS to the appropriate Network State. In order to carry out a network level analysis of a road network that contains many cement concrete lengths of road, it was necessary to derive incremental deterioration models for predicting the performance of this type of pavement. This was achieved by adapting the HDM-4 incremental models used for predicting the rates of deterioration of pavements with stabilised bases. The implementation of this PMS will enable the DPWH to upgrade it current systems and procedures to ensure that expenditure priorities are established by rational methods which maximise benefits from expenditure on the road network. ACKNOWLEDGEMENTS The work described in this paper was carried out by High Point Rendel in association with TRL Limited, UK on behalf of the Department of Public Works and Highways (DPWH) in the Philippines. The project entitled Pavement Management System / Bridge Management System, forms part of the DPWH’s National Roads Improvement and Management Program (Phase 1, NRIMP-1). The project was funded under the Asian Development Bank (ADB) 6th Road Project. The views expressed in this paper are those of the authors and should not be attributed to the sponsoring or collaborating organisations.



AUTHOR BIOGRAPHY Greg Morosiuk has 30 years international experience at TRL working on a variety of problems concerned with road performance and deterioration, the interaction between road condition and vehicle operating costs, whole life costing, road rehabilitation and road investment generally. Experience has been gained throughout the world, from the Caribbean, throughout Africa to South East Asia. He is world renown for his work in the earlier development of vehicle operating cost models and more recently has assumed responsibility for the specifications of the HDM-4 road deterioration and works effects models. In 2002 he was posted to the Philippines for two years primarily to work on the ADB funded project to implement a pavement management system, using HDM-4, for the Philippine national road network being managed by the DPWH.



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04-01-01_PAV_GPH_aplicación-en-Filipinas-HDM-4