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6th International Conference on Managing Pavements (2004)

The AFRICON ICMP6 Pavement Management Investment Analysis Response

I. Wolmarans, G. Robertson, J. Viktor Road Asset Management Section AFRICON Engineering International Pretoria, South Africa

SYNOPSIS

The organising committee of the 6th International Conference on Managing Pavements has challenged asset management professionals on how they can accommodate the interests of infrastructure owners, managers, service providers and the wider community of road users within their specific Pavement Management Systems and practices. This paper contains information regarding the background and analysis findings of the AFRICON ICMP6 Challenge Pavement Asset Management Investment Analysis submission. The AFRICON ICMP6 Challenge Pavement Asset Management System (PAMS) is a sophisticated investment decision support system that is capable of undertaking Life Cycle Cost analyses and heuristic optimization in order to determine optimum road maintenance investments. The system is capable of performing these analyses whilst taking into account a Roads Authority's current or impending budgetary constraints. The sensitivity to various road maintenance policies or funding constraints can be evaluated in terms of their effect on the future road network condition as well as their effect on the broader community (Authority Costs, User Costs). The objective function to be optimized is user defined and by incorporating well defined socio-economic factors into this objective function, it is possible to undertake limited multicriteria assessments. In terms of the investment decision support framework (Table 2.1) developed by Neil Robertson for PIARC in 2002, the AFRICON ICMP6 Challenge PAMS is fully compliant with the level 5 characteristics and has partial compliance as a level 6 system. Highlights of the system and approach are that it has the flexibility to incorporate various pavement deterioration models, optimisation benefit functions, road user cost models, condition indices, treatments and triggers. For the purposes of this submission the system was set up to utilise the HDM4 pavement performance models, simplified road user cost models, an HMD4 distress based composite condition index, the given treatments and suitable triggers. Two options were catered for when selecting the objective function to be optimised (or decision methodology). These are: · · Maximise the Area-Under-the-Condition curve (AUC); or Minimise the Total Transport Cost (TTC) for the local economy.

Four budget scenarios were investigated and the consequences of these are interpreted in terms of the following: · · · · · · · · routine maintenance; the calculated funding values and the allocation of funds between the different maintenance treatments; allocated treatment lengths; funding split between rural and inter-urban roads; predicted network condition; roughness backlog; road user cost and asset value.

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).

6th International Conference on Managing Pavements (2004)

1

INTRODUCTION

The organising committee of the 6th International Conference on Managing Pavements has challenged asset management professionals on how they can accommodate the interests of infrastructure owners, managers, service providers and the wider community of road users within their specific Pavement Management Procedures and Systems. Africon Engineering International has chosen to submit a response to this Challenge in the form of the AFRICON ICMP6 Challenge Pavement Asset Management System (PAMS). This paper contains information regarding the background and analysis findings of our submission. A typical road maintenance needs analysis entails the prediction of the future performance of roads using the current pavement condition and suitable pavement performance models. Furthermore, realistic pavement maintenance and repair alternatives should be considered for each road segment over a 20-year analysis period. The effect of each maintenance/repair alternative is calculated as well as the vehicle operating costs associated with the resultant predicted pavement performance. The outcome of such an analysis are multiyear preventative maintenance and rehabilitation programs for a number of different budget scenarios investigated, whilst optimising the objective function selected for the optimisation analysis. For optimisation, the objective function (or decision methodology) could be selected as either of the following: · · Maximise the Area-Under-the-Condition curve (AUC); or Minimise the Total Transport Cost (TTC) for the local economy.

Both of these potential objective functions are described in this document. Four budget scenarios were investigated and the consequences of these are interpreted in terms of the following: · · · · · · · routine maintenance; the calculated funding values and the allocation of funds between the different maintenance treatments; allocated treatment lengths; predicted network condition; roughness backlog; road user cost and asset value.

2

SYSTEM CLASSIFICATION

The AFRICON ICMP6 Challenge PAMS is a sophisticated investment decision support system that is capable of undertaking Life Cycle Cost analyses and heuristic optimization procedures in order to determine optimum road maintenance investments. The system is capable of performing these analyses whilst taking into account a Roads Authority's current or impending budgetary constraints. The sensitivity to various road maintenance policies or funding constraints can be evaluated in terms of their effect on the future road network condition as well as their effect on the broader community (Authority Costs, User Costs). The objective function to be optimized is user defined and by incorporating well defined socio-economic factors into this objective function, it is possible to undertake limited multi-criteria assessments. In terms of the investment decision support framework (Table 2.1) developed by Neil Robertson for PIARC in 2002, the AFRICON ICMP6 Challenge PAMS is fully compliant with the level 5 characteristics and has partial compliance as a level 6 system. Table 2.1: Classification of decision support levels for road asset management systems Decision Dominant characteristic support level 1 Basic asset data, rule-based work allocation 2 Project and network level assessment, geographic reference 3 Life cycle cost analysis of agency impacts 4 Life cycle cost analysis of agency and user impacts, economic prioritisation 5 Optimum investments within constraints, sensitivity analysis 6 Economic, social, environmental multi-criteria assessment, risk analysis

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).

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3 3.1

SYSTEM FEATURES Pavement Performance Models

To study the long-term consequences of preventative maintenance, rehabilitation and reconstruction actions it is important to use reliable performance prediction models. The HDM4 pavement performance models were programmed into the dTIMSTM CT software and the HDM4 calibration factors supplied for the Challenge were incorporated into these models. These models attempt to describe the complex interaction between vehicles, the environment, pavement structure and condition. Pavement performance is principally a function of the combined effects of traffic and climate. Traffic loads induce stresses and strains within the pavement layers. The magnitude of these responses depends on the load characteristics and the layer thickness and stiffness. The modified structural number, adjusted for the contribution of the subgrade, is used as the measure of pavement strength. Under repeated loads the stresses and strains cause fatigue in bound materials and deformation of all pavement layers. Weathering and solar radiation causes asphaltic/bitumimuous materials to age, become brittle, and more susceptible to cracking and disintegration. Pavement roughness, the main indicator of pavement serviceability, is therefore the result of a chain of distress mechanisms and the combination of various modes of distresses (Table 3.1). This complex interaction between various distress types and the environment is reflected in the pavement performance models used in HDM4. Figure 3.1 shows the complex interaction of these variables graphically. Table 3.1: Distresses modelled by HDM Distress type Description The area of structural cracking, All Cracking >1mm in width. (%) Wide Cracking Ravelling Potholing The area of structural cracking, >3mm. (%) The total area ravelled. (%) The total area of open potholes (minimum depth of 25 mm and diameter of 150 mm). (%) The mean and standard deviation of rut depth. Roughness in IRI

POTHOLES ROUGHNESS CRACKING RUTTING

RAVELLING

Figure 3.1: Interaction of the distress types in the HDM roughness model

Rutting Roughness

3.2

Road User Cost

10.00 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00 1 3 5 7 9

Road user costs were provided for roughness values of 3 (good), 3.5 (fair), 5 (poor) and 6 (very poor). It was assumed that there is a linear progression between any two of the provided values, except for roughness values less than three where it was assumed that the user cost will be the same as that for roughness values of three. A polynomial was fitted through regression on these linear functions to derive the equations for the road user cost. Table 3.2 and Figure 3.2 show the resultant road user cost equations for the different vehicles classes.

Cost: $ / veh.km

Road user costs are primarily associated with the roughness of road pavements. The relationship between road user costs and roughness for the challenge case study was given for each vehicle type in the fleet and for roughness IRI values ranging between 3 and 6. From this given data, regression analyses were carried out to derive suitable road user cost expressions for each vehicle type.

Roughness (IRI) Hvy Rigid Arctic/Trailer

Car 5ax Arctic

Med Rigid 6ax Arctic

4ax Arctic

Figure 3.2: Road user cost versus roughness

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Vehicle class Passenger Car Medium 2 axle Rigid Truck Heavy 3 axle Rigid Truck Heavy 4 axle Rigid Truck Heavy 5 axle Rigid Truck Heavy 6 axle Articulated Truck Heavy, Arctic and Trailer

Table 3.2: Road user cost equations Equation where: RUC = Cost in $ per vehicle km IRI = Roughness in IRI RUCPC = 0.0005×IRI3 - 0.0035×IRI2 +0.0063xIRI + 0.2978 RUCMRD=0.001×IRI3 - 0.0037×IRI2 + 0.002xIRI + 0.8005 RUCHRD=0.0046×IRI3 - 0.0246×IRI2 + 0.0352xIRI + 1.0902 RUC4ax=0.0096×IRI3 - 0.0598×IRI2 + 0.0985xIRI + 1.1685 RUC5ax= 0.0096×IRI3 - 0.0598×IRI2 + 0.0985xIRI + 1.2685 RUC6ax=0.0096×IRI3 - 0.0598×IRI2 + 0.0985xIRI + 1.3685 RUChat= 0.0171×IRI3 - 0.1126×IRI2 +0.1935xIRI + 1.7359

3.3

Composite Condition Index

An algorithm was developed to combine the distresses that are modelled in HDM4 into a composite condition index, ranging from 0% (very poor) to 100% (very good). The condition categories used for reporting condition information are illustrated in Figure 3.3. The composite condition index, is used to describe both the current and predicted future road conditions associated with deterioration and maintenance during the analysis period. The HDM4 distresses shown in Table 3.3 contribute to the value of the condition index. The percentage contribution and maximum allowed value of each distress type are also shown in the table. Table 3.3: Contribution of HDM4 distresses to the condition index Distress % Maximum Contribution value 40% 15.0 All Cracks Wide Cracks Potholes Rutting Roughness 27.5 20.0 17.5 20.0 40% 5% 20mm 9 IRI Figure 3.3: Categories of the condition index

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).

6th International Conference on Managing Pavements (2004)

3.4

Asset Value

Very Good

Value of the road structural layers

Fair Poor

Maximum Theoretical Asset Value of the Road

· · ·

the cost of the ground preparation work, the road foundations and the road structural layers.

Degree of Deterioration

Very Poor

As a road pavement deteriorates over time, the value of its structural layers decreases and reconstruction eventually becomes necessary. When this stage is reached, it can be assumed that the value of the structural layers is zero. The current value of any road is calculated as the value it would have if it was newly constructed, minus the cost of taking it from its present condition to the "very good" condition. The amount subtracted represents the cost of eliminating any deficiency the road may have. This concept is similar to the generally accepted accounting practice of calculating an asset's "book value", which equates to cost minus accumulated depreciation. See Figure 3.4 for an illustration of the asset value calculation.

Value of the road foundation and preparation works

Maximum Theoretical Asset Value - Cost of Restoring to Very Good Condition = Asset Value of a Road

Time

Figure 3.4: Illustration of asset value calculation

3.5

Optimisation Objective Functions

The AFRICON ICMP6 Challenge PAMS has been developed to cater for two possible optimisation objective functions. Depending on the user requirements it is possible to switch between each of the objective functions described below: 3.5.1 TTC: Total Transportation Cost The total transportation cost (TTC) to a society includes both the authority cost of maintaining a road network and the road user cost of operating vehicles upon it. Increased expenditure by the authority, will lead to reduced cost for road users but there is theoretically a level of authority expenditure at which the total transport cost are optimised. The increased spending on maintenance is not necessarily proportional to the savings in road user cost resulting in an optimal maintenance scenario where the total transport cost to society can be minimised. From a purely economical point of view, an authority should maintain a road network at the maintenance level associated with the minimum TTC. See Figure 3.5 for an illustration of optimum total transport costs. Although the maintenance level associated with the optimal TTC should be the objective of a road authority, most authorities are subjected to financial constraints, which result in a scenario where the available budget is less than the optimum maintenance level. This requires an optimisation procedure to determine the set of strategies that will minimise the total transportation cost without exceeding the constrained budget.

Optimum TTC Level Total Transportation Cost

Costs

Road User Cost

Agency Cost

Maintenance Level

Figure 3.5: Illustration of optimum total transport cost

In the analysis the strategies on the network that will lead to the maximum saving in transportation cost within the constrained budget will be identified. For this TTC benefit type the following objective function is therefore maximised:

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).

Asset Value of a Road

Cost of restoring road to a Very Good Condition

The asset value is a measure of the worth of the road network. The value of a pavement is made up of:

Good

6th International Conference on Managing Pavements (2004)

F =

j =1

n

[(RUC

= = = = =

DNj

- RUCij )- COSTS ji

]

Where: F RUCDNj RUCij COSTij n

The objective function/benefit (i.e. the saving in TTC) The Road User Cost in the case of no maintenance on section j The Road User Cost if strategy i is followed on section j The road authority cost associated with strategy i on section j Number of Sections in the analysis

3.5.2 AUC: Area-Under-the-Condition Curve The area-under-the-curve (AUC) benefit is calculated by calculating the difference between the area under the condition index curve resulting from the repair strategy (combination of repair actions over the analysis period) and the area under the condition index curve for the do-nothing alternative for each road section and possible repair strategy. The AUC benefit calculations are then weighted by traffic (Average Annual Daily Traffic (AADT)), so that roads carrying a higher traffic volume receive higher benefits. Figure 3.6 illustrates the method of calculating this benefit.

Repair Strategy

Repair Strategy

Condition Index

Benefit Benefit

Do-Nothing

Analysis Period

Figure 3.6: Area-under-the-condition curve

For this AUC benefit type the following objective function is therefore maximised: F = AADT(i) * ( Str_Cond(i) - Don_Cond(i) ) Where: F i Benefit(i) AADT(i) Str_Cond(i) Don_Cond(i)

= = = = = =

The objective function/benefit (i.e. the condition improvement weighted by traffic) The year in the Analysis Period Benefit of the Strategy for year i AADT on the Element in year i Value for the repair strategy's condition index in year i Value for the do-nothing alternative's condition index in year i

Any strategy that improves the condition would thus have a greater area underneath the curve when compared to the Do Nothing Strategy. This area is then multiplied by the AADT. In this way, roads carrying high traffic receive a higher benefit than those with little traffic.

4

INTERPRETATION OF THE ROAD NETWORK

The road network defined for the Challenge is described in terms of road category, traffic, age and condition. This chapter provides an overview on the current condition and composition of the network.

4.1

Road Category

The total length of the road network is 2508.29 km. The network is categorised into two categories, namely Rural and Inter-Urban. Table 4.1 and Figure 4.1 illustrate the length per road category.

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).

6th International Conference on Managing Pavements (2004)

Table 4.1: Road network by category Category Category description Length (km) % of Network

I R

Inter-urban Roads Rural Roads

441.47 2066.82

18

0 500

82

1000 Length (km) Rural

1500

2000

Inter-urban

Figure 4.1: Length (km) per road category

4.2

Traffic

100% 80%

% Length

The traffic data is based on 2002 information. A 4% increase in heavy freight traffic (articulated vehicles) is expected along the inter-urban sections of the network over the next 20 years, whereas all other traffic is predicted to grow at approximately 2.5% per year. The traffic distribution for 2002 is given in Figure 4.2. The inter-urban roads are the highest trafficked roads with 17% (76 km) of the roads carrying traffic volumes in excess of 7500 vehicles per day. The rural roads carry much less traffic. Forty percent (822 km) of the rural roads have traffic volumes less than 500 vehicles per day. All inter-urban roads have traffic volumes in excess of 500 vehicles per day.

60% 40% 20% 0% Inter-Urban Rural Combined

Very High: AADT 7500+ High: AADT 2500-7499 Medium: AADT 500-2499 Low: AADT 0-499

Figure 4.2: Traffic distribution in 2002

4.3

Condition

100% 80%

4.3.1 Roughness The roughness of roads can be used as a condition indicator. According to "The Challenge Case Study", roads with a roughness of less than 3 IRI are considered to be in good condition, while roads with a roughness of 3 to 4 IRI are in a fair condition. The backlog can be defined as all roads in a poor or very poor condition, or alternatively put; all roads with a roughness value in excess of 4 IRI. Figure 4.3 shows the proportion of the current road condition in terms of roughness. More than 60% (1553 km) of the roads are in a good condition. Currently, the backlog (roads with IRI >4) is 8 % (204 km). The roughness on the Interurban roads is slightly better than the roughness on the rural roads.

% Length

60% 40% 20% 0% Inter-Urban Good: IRI <3 Poor: IRI 4-6 Rural Combined Fair: IRI 3-4 Very Poor: IRI >6

Figure 4.3: Condition distribution in terms of roughness

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).

6th International Conference on Managing Pavements (2004)

4.3.2 Composite Condition Index The results for the composite condition index are similar to those for roughness. Fifty seven percent (1439 km) of the roads are in a good condition. The remainder of the roads are in a fair condition. The condition on inter-urban roads is slightly better than the condition on rural roads. See Figure 4.4 for the condition distribution.

100% 80%

% Length

60% 40% 20% 0% Inter-Urban

Very Good Good

Currently the network has an overall network condition of 70%. The average condition of Rural roads is 69% compared to 72% for Inter-Urban roads.

Rural

Fair Poor

Combined

Very Poor

Figure 4.4: Composite condition index distribution

4.4

Pavement Ages

42%

The distribution of pavement ages is an estimation of the future pavement replacement demand that can be expected. A pavement has a limited life span that depends on the maintenance it receives over its life. Repair (rehabilitation or reconstruction) is inevitable to ensure the continued functioning of the pavement. Figure 4.5 shows the distribution of the pavement ages on the roads. Almost half (1234 km) of the roads were built in the last six years, 42% (1059 km) are between 6 and 12 years old and 9% (215 km) are older than twelve years. More than 90% of the network was built during the last 12 years.

9%

49%

Age: <6 years Age: >12 years Age: 6-12 years

Figure 4.5: Pavement age distribution

5

THE ANALYSIS

This section presents the results of the data investigation prior to the analyses, with a particular focus on the maximum length of road sections, the committed works and the budget scenarios. Appendix A contains three tables describing the analysis input information as used in the dTIMSTM CT software.

5.1

Optimisation Objective Function: TTC versus AUC

The objective function determines how the benefit is calculated for each of the multi-year repair alternatives per analysed road section. The two objective functions; maximise Area-Under-the-Condition curve (AUC) and minimise Total Transport Cost (TTC) considered for this study are detailed in 3.5.2. For the AUC approach, benefits in terms of condition are easy to achieve as all repair alternatives considered during this study result in an improvement in condition, and an improvement in condition represents a benefit. This optimisation approach is typically used on road networks where the traffic volumes are quite small and where the economic benefits (TTC) of maintenance are not always justified. It is therefore an approach that aims to maximise the overall network condition, which in turn translates to preservation of the road network asset value. This approach is also recommended to calculate the minimum cost required to maintain the network according to a set of minimum standards.

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).

6th International Conference on Managing Pavements (2004)

The TTC approach relies heavily on economics and benefits are only generated on roads where the savings in road user cost (RUC) exceed the cost stream of the repair strategy. This optimisation approach is typically used on road networks where the traffic volumes are high and where the economic benefits (TTC savings) of maintenance are well justified. It is therefore an approach that aims to maximise the overall economic savings in total transportation costs which in turn translates to minimisation of TTC. The net present value of both the savings in RUC and the expenditure, over the twenty-year analysis period, is used. Road user costs are proportional to the traffic on a road, generating higher savings in road user cost on high trafficked roads and vice versa. Furthermore the savings in road user cost are also dependant on slope of the road user cost model and the initial roughness value. The road user cost model and TTC approach for this study are detailed in 3.5.1. Initially an analysis was conducted to establish whether the network could be preserved when the Total Transport Cost is minimised for the network. No limit was placed on the available funding thereby calculating the optimum overall network condition and the optimum expenditure for the TTC benefit function.

5.1.1 Consequences of Minimising TTC The analysis results showed that a total of 2 295 km (92%) of the network has economic merit for maintenance over the twenty-year analysis period other than routine maintenance only. It is however very important to note that due to the high cost of reconstruction, fewer than 30km of these roads showed economic merit for reconstruction over the twenty-year analysis period. This approach shows no benefit in replacing pavement structures at the end of their lives. For 8% of the network the analysis has selected either routine maintenance only or no maintenance at all. It was determined that an optimum budget of $259 million for the 20 year analysis period would be required to minimise the Total Transport Cost for the network.

Figure 5.1 shows that in the long-term, the composite condition index of the network will steadily decrease from 70% in 2004 to 65% after twenty years. The TTC approach is thus not suitable to determine maintenance strategies that will maintain the condition and asset value of the network of the It was therefore decided to road authority. complete all further analyses for this study with the objective being to maximise the overall network condition (AUC approach), thereby preserving the road network as an asset for the road authority.

100 90 80 70 60 50 40 30 20 10 0 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022

Condition (%)

Year

Figure 5.1: Predicted network condition for the TTC analysis (Unlimited funding)

5.2

Budget Scenarios Investigated

The outcome of the needs analysis is highly dependant on the funding available for maintaining the network and all results are thus expressed as the consequence of a budget scenario. The ICMP6 Challenge requires the consequences of the two twenty-year budgets of $200 million and $300 million respectively to be investigated. Apart from these two budgets, two additional funding scenarios were added to the analysis to investigate other possible outcomes. An additional Minimum Standards Budget was added to calculate the minimum cost required to maintain the network roughness below a value of 4.0 IRI. This would provide a forward plan aimed at meeting this specific target roughness condition and maintaining it on all roads. The Optimum Budget scenario was added to calculate the funding required to optimally maintain the road network, in the best possible condition, and reconstruct all roads at the end of their lives. Table 5.1 describes the four budget scenarios investigated during this need analysis. The names under the Budget Scenario Name column are used throughout the rest of the report when referring to the budget scenarios.

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Table 5.1: Budget scenarios Budget Scenario Name Description

$200 Million Budget $300 Million Budget Minimum Standards Budget Optimum Budget

A total budget of $200 million annualised over 20 years. A total budget of $300 million annualised over 20 years. The analysis calculates the MINIMUM required funding to maintain the network at a minimum standard which in this case is defined as ensuring that no roads are in a poor or very poor condition over the twenty-year analysis period. Poor and very poor roads are classified as having a roughness value in excess of 4.0 IRI. The analysis calculates the optimum required funding to maximise the overall condition for the network. All roads are optimally maintained, with reconstruction at the end of their lives, with no funding limitations.

All four budgets were optimised with the objective function to maximise condition (AUC).

5.3

Externally Imposed Committed Work

One of the conditions of the analysis problem is to ensure that all road sections in a poor or very poor condition, and carrying more than 7500 vehicles a day should be treated in the first three years of the analysis period. This condition was investigated for its impact on the funding that would be required to repair the backlog of repairs. For the purpose of this report backlog refers to all road sections with roughness values more than 4.0 IRI. According to The Challenge's data set, a poor or very poor condition is associated with a roughness rating of 4.0 IRI or more. A total road length of 203.08 km was identified as being in a poor or very poor condition according the roughness values. A very small proportion of these sections carry traffic in excess of 7500 vehicles per day and only 4.19 km of the sections were identified as being applicable to this externally imposed committed work. This is less than 0.2% of the network length. Table 5.2 contains the detail of these road sections requiring committed works within the first three years of the analysis. The sections were analysed as a sub-network during the initial stages of the study to determine the cost that will be required for such committed work. It was found that in all cases the alternative to reconstruct, with appropriate follow-up maintenance work, yields the most benefit in terms of condition and cost. The cumulative cost to repair the eight sections was calculated at $1.303 million. In comparison to the available funds of the annualised $200 Million and $300 Million budgets, this cost is relatively small and can easily be absorbed into the annual cash flow. The analyses will thus include the set of standards ensuring all poor and very poor roads carrying more than 7500 vehicles a day will be repaired within the first three analysis years.

Table 5.2: Road sections requiring commmitted works within the first three years of the analysis period Section name Section Length (km) Width (m) AADT Roughness Pavement Reconstruction (IRI) age (years) cost

I-2LS-VH-VP-<6yrs I-2LS-VH-P-12+yrs R-2LS-VH-VP-6-12yrs R-2LS-VH-P-6-12yrs R-2LS-VH-P-12+yrs I-2LS-VH-P-6-12yrs I-2LS-VH-P-<6yrs R-2LS-VH-P-<6yrs

0.03 0.63 0.14 0.76 0.11 0.55 1.42 0.55 4.19

12.00 7.49 10.50 8.55 7.40 8.17 7.62 7.40

12,989 12,352 12,248 11,059 10,982 10,906 10,479 8,546

7.71 5.13 6.72 4.89 5.03 4.16 4.16 4.32

6 14 11 9 20 9 2 3

$16 200 $212 341 $44 100 $194 940 $24 420 $202 208 $486 918 $122 100 $ 1303 227

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).

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5.4

Section Lengths for Analysis

The road sections of The Challenge database ("representative road sections") range in length from 0.02 km to 297.35 km. In total 90% of the network has a section length of more than 20 km, whilst 60% of the network has a length of more than 100 km. Depending on the road category and repair type, a road section of 100 km requires between $10 million and $35 million to repair. This expenditure is very large especially when it is compared to the available expenditure of the two given budgets, $200 million ($10 million annualised) and $300 million ($15 million annualised). These larger sections would be overlooked for repair when optimisation determines the maintenance and rehabilitation plans, because the required funding for repair exceed the allowable funding in any one year of the annualised budget. Thus, in order to produce realistic project plans it was essential that the road network be divided into more realistic analysis sections. The PAMS software, dTIMSTM CT has a dFRAG module, which is used to automatically divide, or combine sections according to the user's set of criteria. In this case the road sections of The Challenge database were divided into smaller sections, never exceeding a maximum length of 10 km. The maximum funding required to repair a 10 km section is approximately $3.5 million which is much more realistic in terms of annualised expenditure. The PAMS software also has the ability to distribute any repair treatment over a period of more than one year, depending on the practical construction that can be achieved within a year.

6

CONSEQUENCES

The consequences of the four budget scenarios are interpreted in terms of the following:

· · · · · · · ·

routine maintenance; the calculated funding values and the allocation of funds between the different maintenance treatments; allocated treatment lengths; funding split between rural and inter-urban roads; predicted network condition; roughness backlog; road user cost and; asset value.

6.1

Routine Maintenance

Table 6.1: Routine maintenance funds Total required funds in $ million over 20 year period Budget scenario name Drainage Edge Repair Crack Seal Patching Total 35.58 56.46 96.97 71.75

The distribution of funds for routine maintenance is shown in Table 6.1and Figure 6.1.

$200 Million Budget $300 Million Budget Minimum Standards Budget Optimum Budget

16.81 32.02 50.08 49.29

14.82 20.18 20.88 20.75

3.90 4.26 23.37 1.70

0.04 0.00 2.64 0.00

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The average annual required routine maintenance funds, per road kilometre, was calculated for the four budget scenarios:

· · · ·

Optimum Minimum Standards $300 Million $200 Million

0 10 20 30 40 50 60 70 80 90 100

$200 Million Budget: $300 Million Budget: Minimum Standards: Optimum Budget:

$709 / km per annum $1 125 / km per annum $1 933 / km per annum $1 430 / km per annum

Total Cost ($ Million) Drainage CrackSeal EdgeRepair Patching

Figure 6.1: Total funding for routine maintenance, 2004 to 2023

6.2

Fund Allocation

Optimum Minimum Standards $300 Million $200 Million

0 100 200 300 400 500 600

Figure 6.2 compares the calculated funding requirements of the Minimum Standards Budget and the Optimum Budget to the $200 Million and $300 Million Budgets.

Total Cost ($ Million) Routine Maintenance Thin Asphalt Overlay Reconstruction Reseal Structural Overlay

Figure 6.2: Total funding per treatment, 2004 to 2023 6.2.1 Funding to Maintain Minimum Standards A minimum twenty-year budget of $358 million is required to maintain the entire network with no roads in a poor or very poor condition, thus all roads have roughness values less than 4.0 IRI. Only 5% of this budget is allocated for reconstruction, whilst 50% of the funding is assigned to structural overlays. Another 27% of the funding is allocated to routine maintenance. The allocation of funding is such to achieve minimum standards with minimum cost.

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).

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6.2.2 Optimum Funding The Optimum Budget, where the entire network is optimally maintained, requires a very high budget of $629 million over the twenty-year analysis period. This budget scenario has the objective of maximising the overall condition for the network. All roads are optimally maintained, with reconstruction at the end of their lives and with no funding limitations. The structural life depends on the age of the road and the most recent assessed condition. Figure 6.3 illustrates how the analysis calculated the average life of pavements in the database between 16 and 22 years.

1986 1988 1990 1992 1994

1996 1998 2000 2002 2004 2006

Length constructed Length of structural repair: Optim um Budg Figure 6.3: Construction versus selected repair lengths 6.2.3 Fund Allocation Figure 6.4 illustrates the importance of routine and preventative treatments. The percentage of the total funding allocated to routine maintenance and preventative treatments (thin asphalt overlay and reseals) increase as the budget decreases. Thus, if funding is limited, the network is preserved with routine and preventative maintenance treatments. The proportion of total funding spent on routine and preventative treatments are as follows:

· 56% for the $200 Million Budget, · 54% for the $300 Million Budget, · 45% for the Minimum Standards Budget and 30% for the Optimum Budget.

Optim um Minim um Standards $300 Million

$200 Million 0% 20% 40% 60% 80% 100%

Total Cost ($ Million) Routine Maintenance Reseal Thin Asphalt Overlay Structural Overlay Reconstruction

Figure 6.4: Funding distribution, 2004 to 2023 Proportionally the Minimum Standards Budget allocated more funding to structural overlays than the rest of the budget scenarios. This is because this repair treatment is cheaper than a reconstruction treatment whilst the roughness can also be limited to below 4.0 IRI.

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).

2008 2010 2012 2014 2016 2018 2020

900 800 700 600 500 400 300 200 100 0

16 to 22 years

Length (km)

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6.3

Allocated Treatment Lengths

Optimum Minimum Standards $300 Million $200 Million

0 50 100 150 200 250 300

The annualised lengths for preventative and repair treatments are shown in Figure 6.5. Routine maintenance is not included in these results. The increase in length of reseals and structural overlays is clear when comparing the $200 Million Budget to the $300 Million Budget. Proportionally a large proportion of the network is recommended for structural overlays for the Minimum Standards Budget, being the most cost effective alternative over a pavement life of more than 15 years and reducing the roughness values well below the threshold value of 4.0 IRI. This is in line with this budget's objective to minimise cost whilst maintaining the network at minimum standards. The structural pavement repair frequency is also an indication of the potential of a budget to conserve a road network as an asset. The repair frequency is the average age of a pavement before receiving structural repair. From Table 6.2 it is clear that the frequency for structural repair achieved with the Optimum Budget and is very close to the standard design life of pavements (i.e. 20 yrs). The structural repair frequencies of the $200 Million and $300 Million Budgets are well beyond acceptable maintenance practices.

Annualised Length (km) Reseal Structural overlay Thin Asphalt Overlay Reconstruction

Figure 6.5: Treatment distribution, 2004 to 2023 Table 6.2: Expected repair cycles Structural pavement Budget scenario repair frequency (years) $200 Million 113 $300 Million 71 Minimum 47 Standards Optimum 22

6.4

Funding Split: Rural versus Inter-urban

100% 80%

Proportion of funds

The four budgets were also investigated for the split of funds between rural and inter-urban roads. The 441km (18%) of inter-urban roads; carrying predominantly medium to very high traffic, are in a slightly better condition than the rural roads. Rural roads comprise 2067km (82%) of the network, carrying low to medium volume traffic. The split of funding is dependant on the budget. If funding is limited, the proportion of funds spent on the 18% inter-urban roads is high, namely 48%. This is a direct result of the optimisation process favouring high traffic roads. When the budget is increased, the proportion of the funds spent on rural roads increases. This trend is clear in Figure 6.6; the higher the budget, the higher the proportion of funding becomes for the low to medium traffic rural roads. For the Optimum Budget the proportion of funds and network split is almost identical. The proportional spending of the Minimum Standards Budget is less for inter-urban roads when compared to the other budgets. This is because the optimisation minimised cost, rather than maximising the condition weighted by traffic. The allocation of funds matches the physical length split between rural and inter-urban roads in this case.

105

195 292 496

60% 40% 20% 0% 99

106 65 133

$200 Million Inter-urban

$300 Million

Minimum Optimum Standards Rural

Figure 6.6: Funding split: rural versus inter-urban

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).

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6.5

Predicted Network Condition

100 90 80 70 60 50 40 30 20 10 0 2004 2006 2008 2010 2012 2014 2016 2018

2018

2020

Figure 6.7 shows the overall network predicted condition index for the different budget scenarios. The current condition for the network is 70%. Neither the $200 Million nor the $300 Million Budget can maintain the current condition of the network and both budgets result in the deterioration of the network. The expected decrease in overall network condition is 40% and 15%, for the $200 Million Budget and $300 Million Budget respectively. The Minimum Standards Budget maintains the overall network condition at approximately 75%, whilst the Optimum Budget will maintain the overall condition of the network between 80% and 85% over the next twenty years.

Condition (%)

$200 Million Minimum Standards

$300 Million Optimum

Figure 6.7: Average network condition

6.6

Roughness Backlog

2500 2250 2000 1750 1500 1250 1000 750 500 250 0 2004 2006 2008 2010 2012 2014 2016 2018 100 90 80 70 60 50 40 30 20 10 0

Both the Minimum Standards Budget and the Optimum Budget eliminate the roughness backlog, whilst the two investigated budgets, $200 Million and $300 Million, are inadequate to maintain the network without a roughness backlog. In 2020 the predicted roughness backlog would have increased from 10% to:

· ·

25% for the $200 Million Budget, and 20% for the $300 Million Budget.

Length (km)

$200 Million Minimum Standards

$300 Million Optimum

Figure 6.8: Network length not complying to a minimum roughness value of 4.0 IRI

6.7

Road User Cost

120 100 80

The effects of budget increases were investigated with regards to the potential savings to road users. Figure 6.9 uses the 200 million budget as the base and compares the savings in road user cost against the increase in annual budget of the three highest budgets. Even though the TTC benefit function was not used during the optimisation process, savings in road user cost are expected with an increase in funding. The potential savings in road user cost of the Optimum Budget is less than the Minimum Standards Budget. The Optimum Budget selected reconstruction in stead of structural overlays and the deterioration in terms of roughness is therefore more severe before a road is repaired. Increased roughness is associated with increased road user costs and the potential savings in RUC is thus lower for the Optimum Budget.

$ Million

60 40 20 0 2004 2006 2008 2010 2012 2014 2016 2020

$300 Million Minim um Standards Optim um

Figure 6.9: Cumulative road user cost relative to the $200 million budget

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).

Length (%)

Figure 6.8 shows the length and percentage of roads exceeding a roughness value of 4.0 IRI. These are the roads with a poor or very poor roughness condition rating.

2020

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6.8

Asset Value

100 Change in Current (2004) Asset Value ($ Million) 50 0 -50 -100 -150 -200 2004 2007 2010 2013 2016 2019

The $200 Million and $300 Million Budgets cannot increase the asset value of the road network relative to 2004. A road network is a valuable asset for the road authority and it is the authority's responsibility to sustain the network. After 15 years the asset value decrease is estimated at $170 million if the $200 Million Budget is implemented. The decrease in asset value if the $300 Million Budget is implemented is estimated at $60 million after 15 years. The Minimum Standards and Optimum Budgets will both increase the asset value of the network, due to the improvement in overall network condition. The increases are estimated at $30 million and $75 million respectively by 2020.

`

$200 M illion M inim um Standards

$300 M illion Optim um

Figure 6.10: Change in asset value compared to 2004

7

CONCLUSION

This report describes the results of the ICMP6 Pavement Management Investment Analysis Challenge. The consequences of two twenty-year budgets namely, $200 million and $300 million were investigated. Apart from these two budgets, two additional funding scenarios were added to the analysis to investigate other possible outcomes. A Minimum Standards Budget scenario was added to calculate the minimum cost required to maintain the network roughness below a value of 4.0 IRI. This would provide a forward plan aimed at meeting this specific target roughness condition and maintaining it on all roads. The Optimum Budget scenario was added to calculate the funding required to optimally maintain the road network, in the best possible condition, and reconstruct all roads at the end of their lives. The system's features and analysis input include the following important items:

· · · ·

All roads in a poor or very poor condition and carrying traffic in excess of 7500 vehicles per day were identified as externally imposed committed work and repaired within the first three years of all the analyses. The road network was divided into realistic analysis sections of 10 km length maximum. All analyses were completed with the objective to maximise the condition index, thereby preserving the road network as an asset for the road authority. A composite Condition Index as developed by Africon was used in the study to report on the expected future condition associated with deterioration and maintenance during the analysis period.

Table 7.1 describes the four budget scenarios investigated during this need analysis. It also shows the allocated funding and the predicted pavement repair frequency. Figure 7.1, Figure 7.2, Figure 7.3 and Figure 7.4 show the principle consequences of the four budget scenarios.

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).

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Table 7.1: Budget scenarios Budget Scenario Name $200 Million Budget $300 Million Budget Minimum Standards Budget Optimum Budget Description Twenty-Year Budget Structural Pavement Repair Frequency (in years) 113 71

A total budget of $200 million annualised over 20 years. A total budget of $300 million annualised over 20 years MINIMUM required funding to maintain the network according to the minimum standards that no poor or very poor roads are allowed. Poor and very poor roads are classified as having a roughness value in excess of 4.0 IRI The analysis calculates the optimum required funding to maximise the overall condition for the network. All roads are optimally maintained, with reconstruction at the end of their lives, with no funding limitations.

$200 Million $300 Million

$358 Million

47

$629 Million

22

All four budgets were optimised with the objective function to maximise condition (AUC).

Optimum Minimum Standards $300 Million $200 Million

0 100 200 300 400 500 600

100 90 80 70 60 50 40 30 20 10 0 2004 2006 2008 2010 2012 2014 2016 2018 2020

2019

Total Cost ($ Million) Routine Maintenance Thin Asphalt Overlay Reconstruction Reseal Structural Overlay

Condition (%)

$200 Million Minimum Standards

$300 Million Optimum

Figure 7.1: Total funding per treatment, 2004 to 2023

Figure 7.2: Average network condition

Change in Current (2004) Asset Value ($ Million)

2500 2250 2000 1750 1500 1250 1000 750 500 250 0

2004 2006 2008 2010 2012 2014 2016 2018 2020

100 90 80 70 60 50 40 30 20 10 0

100 50 0 -50 -100 -150 -200 2004 2007 2010 2013 2016

Length (km)

Length (%)

`

$200 M illion Budge t $300 M illion Budge t M inim um Standards Budge t Optim um Budge t

$200 Million Minim um Standards

$300 Million Optim um

Figure 7.3: Network length not complying with a minimum roughness value of 4.0 IRI

Figure 7.4: Change in asset value compared to 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).

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$200 Million Budget: The total twenty-year budget of $200 million is inadequate to maintain the network in its current condition. If the network is maintained with this budget it is expected to deteriorate with a predicted drop in the condition index from 70% in 2004 to 32% in 2020. The expected backlog of roads with a poor or very poor roughness is 25% (625 km) by the year 2020, which will require additional funding in excess of $140 million for reconstruction. This budget cannot maintain the road network as a valuable asset for the road authority and the decrease in asset value is estimated at $170 million by 2020. $300 Million Budget: The total twenty-year budget of $300 million is also inadequate to maintain the network in its current condition. The expected deterioration in the overall condition index is 15%, from 70% in 2004 to 55% in 2020. The expected backlog of roads with a poor or very poor roughness is 20% (500 km) by the year 2020, requiring additional funding in excess of $100 million for reconstruction. The road network cannot be maintained as a valuable asset for the road authority and the decrease in asset value is estimated at $60 million by 2020. Minimum Standards Budget: The Minimum Standards Budget maintains the overall network below a threshold roughness value of 4.0 IRI, at the minimum cost. For this funding scenario, the analysis predicted that the network would be maintained at an overall condition of 75%. It can thus be expected that this budget will improve the overall condition of the network. No backlog is expected of roads with a poor or very poor roughness. This budget results in an improvement of asset value for the road authority. The funding requirement to meet this objective is $358 million for a twenty-year period. Optimum Budget: The Optimum Budget is the hypothetical case where no fund limitations exist and where roads are reconstructed at the end of their lives. This budget is sufficient to maintain the overall condition of the network between 80% and 85% over the next twenty years. No backlog of roads with a poor or very poor roughness is expected. This budget results in an improvement of asset value for the road authority. The funding requirement to meet this objective is $629 million for a twenty-year period. It is the recommendation of this study that the network examined in this study be maintained by a budget in excess of $358 million over the next 20 years. The calculated annual budget should therefore be $18 million or more. The minimum standards in terms of the roughness threshold can be met and an overall improvement in condition, with the consequential avoidance of loss in asset value will follow.

8

REFERENCES

HDM-4 Technical Reference Manual; The Highway Development and Management Series, Volume 4; The World Road Association (PIARC), 1999.

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BIOGRAPHIES OF PRESENTING AUTHORS

Johan Viktor Johan's highest qualification is a B.Eng (Hons) (Civils) obtained at the University of Pretoria, South Africa in 1985. Since 1987, Johan has specialised in the field of road management systems and has been involved in the development, implementation and operation of systems for various road authorities throughout South Africa, Lesotho and Kenya. These road management systems included inter alia pavement management systems, unpaved road management systems and maintenance management systems. Road authorities worked for include several provinces of South Africa (the Department of Public Works and Roads of the North West Province, the Department of Public Works of the Eastern Cape Province, the Department of Public Works of the Northern Province, the Department of Public Transport and Roads of the Gauteng Province), the Ministry of Works of Lesotho and Ministry of Local Government of Kenya. He is also currently involved in the letting and supervision of routine maintenance contracts on national roads for the National Roads Agency. Prior to 1987 Mr Viktor worked primarily as a pavement/materials engineer and is therefore fully acquainted with the principles of road construction and consequent preventative and routine maintenance needs. Gordon Robertson Gordon's highest qualification is a B.Sc (Hons) (Civil) obtained at the University of the Witwatersrand, South Africa in 2002. Since 1997, Gordon has specialised in the field of integrated pavement management and geographic information systems (GIS) and has been involved in the development and implementation of systems for various road authorities throughout Southern Africa. He has a keen interest in the execution of field surveys ranging from visual condition assessments, GPS inventory surveys, mechanical riding quality measurements, deflection measurements and digital video logging. Specific projects on which Gordon has been involved are the development, implementation and support of systems for:

· · · ·

the paved and unpaved roads of the Eastern Cape, Northern Cape and KwaZulu Natal Province's (South Africa), the National Route 3 and National Route 4 Toll Road Concessions (South Africa), the paved and unpaved roads of the Roads Departments of Swaziland, Botswana, Lesotho and Zambia, the calibration of HDM-III pavement deterioration models for Botswana.

Currently, in the field of urban road management, Gordon is the Project Leader for the implementation of a fully integrated Road Management System for the City of Durban (South Africa). The project includes the development of new approaches to assessing and managing segmented block pavements, jointed concrete and unpaved roads in an urban context. The application of HDM based pavement deterioration models has been implemented and successfully tested on this system

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APPENDIX A Input to the Analysis

Table A.1 through A.3 contain the analysis input information. The PAMS uses the 2002 condition and traffic data, to automatically calculate the 2004 values at the start of the analysis period. Table A.1: Analysis information Date of condition data Date of traffic data Analysis period Discount rate 2002 2002 2004 ­ 2023 (20 years) 7%

Table A.2: HDM model calibration factors Deterioration model Wet/Dry Season SNP Ratio Drainage Factor All Structural Cracking ­ Initiation Wide Structural Cracking ­ Initiation All Structural Cracking ­ Progression Wide Structural Cracking ­ Progression Rutting ­ Initial Densification Rutting ­ Plastic Deformation Ravelling ­ Initiation Ravelling ­ Progression Pothole ­ Initiation Pothole ­ Progression Edge Break Roughness ­ Environmental Coefficient Roughness ­ SNPK Roughness ­ Progression Calibration factor Kf Kddf Kcia Kciw Kcpa Kcpw Krid Krpd Kvi Kvp Kpi Kpp Keb Kgm Ksnpk Kgp Inter-urban 1.0 1.0 (No effects) 1.1 1.1 0.3 0.3 0.5 1.3 2 0.5 2 0.25 1 0.3 1.0 0.3 Rural 1.0 1.0 (No effects) 0.9 0.9 0.3 0.3 0.5 2.5 2 0.5 2 0.25 1 0.6 1.0 0.6

Table A.3: Treatments, triggers and costs Description Routine drainage Routine crack sealing Routine structural patching Routine edge repairs Bituminous surface dressing, initial & reseals Thin asphalt surfacing (35 mm AC) Cost $1,000/km/year $20/m2 $65/m2 $9/m2 Trigger Annually, except for the year when a reconstruction is done. All cracks > 5%. Potholes > 0.05%. Edge break > 2% of total carriage way area (Rural roads Or Inter-Urban with AADT 2000) And (All cracks > 5% Or Ravelling > 5%) Inter-Urban with AADT > 2000 And (All cracks > 5% Or Ravelling > 5%) Limiting value Wide cracks < 10%. Ravelling < 5%. Roughness < 5 IRI. Roughness < 6 IRI.

$3/m2

Roughness 4 IRI Wide cracks 10 % Condition Index > 40%

$14/m2

Roughness < 5 IRI Wide cracks < 10 % Condition Index > 40%

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Table A.3: Treatments, triggers and costs Description Structural overlay (80 mm AC) Cost $28/m2 Trigger Wide cracks 10% Or Roughness 4 IRI Or Rut depth > 12 mm Inter-Urban Roads And (Roughness 4.5 IRI Or Rut depth 20mm Or Wide cracks > 20% Or Condition Index 40% Or Pavement Age 25years) Rural roads And (Roughness 4.5 IRI Or Rut depth 20mm Or Wide cracks > 20% Or Condition Index 40% Or Pavement Age 25years Limiting value Roughness 7.5 IRI Rut depth 20mm Pavement age 25yrs

Pavement ReconstructionInter-urban 200 mm Granular and 50 mm AC

$45/m2

Pavement ReconstructionRural 150 mm Granular and DBSD

$30/m2

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