ABSTRACT: Ireland has a national road network of approximately 5300km which is managed by Transport Infrastructure Ireland (TII). TII create yearly pavement maintenance and renewal programmes using a Pavement Asset Management System (PAMS). Accurate pavement deterioration models and reset values are an essential part of a PAMS and are required for life cycle cost analysis (LCCA). Pavement maintenance and renewal schemes are prioritised based on the LCCA's largest benefit to cost ratio, thereby optimising the annual pavement maintenance and renewal budget. Currently, TII implement pavement deterioration models based on models used in Austria and Belgium, with modifications appropriate to Irish conditions. The reset values in use are based on best estimates of expected treatment effects and were not initially based on the measured post-treatment condition. This research aims to refine and improve the deterioration models currently in use and to calibrate the treatment reset values using measured pavement condition on treated sections of pavement. Several long-term pavement performance (LTPP) monitoring sites were selected for the analysis. The LTPP monitoring sites consist of newly constructed and recently maintained pavement sections throughout the Irish national road network for each subnet category. The international roughness index (IRI), rut depth and longitudinal profile variance 3m (LPV3), measured annually from 2010 to 2019, were analysed and compared to the current PAMS's pavement reset values and deterioration models. Generally, the measured reset values of the LTPP monitoring sites were lower than the TII's PAMS reset values, indicating that TII's PAMS may underestimate the treatment benefit for the LCCA treatment prioritisation process. The measured pavement deterioration rates were also typically lower than the predicted pavement condition values of TII's PAMS. This research concludes that an update to the PAMS, using better calibrated deterioration models and reset values would be highly beneficial. The LTPP monitoring should be continued to assess the longevity of the applied pavement treatments and consequently, determine the full deterioration model for each of the LTPP monitoring sites into the future.

KEY WORDS: Pavement asset management system, life cycle cost analysis, international roughness index, rut depth, longitudinal profile variance, HDM modelling, pavement deterioration modelling

1 INTRODUCTION

Ireland has a national road network of approximately 5300km that is managed by Transport Infrastructure Ireland (TII). Pavement condition monitoring surveys have been carried out annually on the full network since 2010. TII’s Pavement Asset Management System (PAMS) is used to select appropriate pavement treatment schemes, according to the annual pavement condition surveys and budget. TII’s PAMS applies reset values and pavement deterioration models to the road network based on measured condition data. TII’s PAMS triggers sections of pavement for treatment, either from the current or predicted pavement condition values and prioritises them based on the best life-cycle cost analysis (LCCA) benefit to cost ratio, thereby maximising the monetary value of the annual pavement treatment budget. TII’s PAMS pavement reset values and pavement deterioration models are based on modified Austria and Belgium models. The IRI, rut depth and LPV3 pavement deterioration models are displayed in equations 1, 2 and 3 respectively, and the equation constants A and B are selected according to the national road subnet category [1].

The correlation between the actual pavement condition, measured annually since 2010, and the PAMS reset values and pavement deterioration models were investigated through the analysis of TII’s road pavement condition archive. TII’s pavement treatment programme has an approximate annual budget of €100 million. Therefore, this type of research has the potential to improve the treatment prioritisation process of TII’s PAMS LCCA analysis and maximise the efficiency of the annual treatment budget. In recent decades, the improvement in computing technology has made the quantitative modelling of pavement assets possible. Previously, in the absence of computing technology, pavement network management has relied on expert opinion and judgment to maintain and improve the value of the pavement assets. This approach was typically known as a ‘find and fix’ approach, which has now been superseded by the ‘predict and prevent’ approach of deterioration modelling [2]. The ‘predict and prevent’ pavement modelling method predicts how pavement assets deteriorate under specific condition parameters, like traffic, subgrade conditions, environmental

𝐼𝑅𝐼 = 𝐼𝑅𝐼 + 𝐴 + 𝐵 𝑥 𝑇𝑟𝑎𝑓𝑓𝑖𝑐 (1)

𝑅𝑢𝑡 𝐷𝑒𝑝𝑡ℎ = 𝐴 𝑥 𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒𝑇𝑟𝑎𝑓𝑓𝑖𝑐 (2)

LPV3t = LPV3

t-1 + 𝐴 𝑥 𝑇𝑟𝑎𝑓𝑓𝑖𝑐 (3)

Analysis and Optimisation of TII’s PAMS Reset Values and Deterioration Models for Irish Pavement Conditions

James Quinn1,2, Raymond McGowan1, Tomas O’ Flaherty2, 1PMS Pavement Management Services Ltd. Raheen Industrial Estate, Athenry, Galway, H65 PD37 Ireland

2 Department of Civil Engineering, Institute of Technology Sligo, Ash Lane, Sligo, F91 YW50 Ireland email: jamesquinn@pms.ie, raymcgowan@pms.ie, oflaherty.tomas@itsligo.ie

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conditions, etc. and many dataset sources can be used to determine pavement deterioration models. These dataset sources can include historical pavement performance and failure records, pavement condition measurements of current and past pavement conditions, pavement design and construction details and pavement expert opinion and knowledge [2]. It then becomes evident that deterioration models are a critical tool in PAMS, which incorporate economic appraisal concepts for managing the road network. PAMS can be used for maintenance decisions, network reliability, operation and safety, maintenance scheduling and optimisation of budgetary allocation. They also ensure that an empirically informed decision-making process is produced for pavement asset investment strategies, which results in a numerically determined pavement asset investment strategy. A $150 million research programme introduced LTPP to the world with the inclusion of 370 LTPP sites across the United States in 1987 [3]. In addition, for the past twenty years, long-term pavement performance (LTPP) has been used to determine the optimal pavement maintenance and capital funding requirements for the Australian road network. The original Australian pavement deterioration models were implemented from pavement data modelling in Brazil, the Caribbean and Kenya, and did not specifically incorporate Australian conditions [4]. As unsealed gravel surface course pavements are typical pavement construction techniques found in Australia, New Zealand and South Africa the Brazilian, Caribbean and Kenyan pavement deterioration models were not likely to suit the unique conditions of sealed and unsealed pavement surfaces, construction techniques, climate and ground conditions in Australia [4]. Pradham & Mallela [5] stated that the Highway Design and Management (HDM) pavement deterioration models should be customised for local pavement conditions. This customisation and calibration of the HDM models can be split into three levels of optimisation [5]:

Level 1: Basic Application

Level 2: Verification

Level 3: Adaptation.

Level 1 is the lowest level of calibration and should be implemented during the installation phase of the HDM pavement deterioration models. Default values incorporated from the HDM models and secondary source data from desk studies are acceptable for Level 1 calibration [6]. Even with the adaption of default and secondary values, the most sensitive parameters must still be estimated. The sensitive parameters include unit costs for road user effects and road deterioration and works effects, characteristics of representative vehicles, discount rates and analysis period for economic analysis data, road deterioration and works effects of pavement characteristics, traffic and projected growth rates, and climate [6]. Level 2 HDM model calibration incorporates physical measurements of local conditions into the model predictions. The road user effects that are calibrated for Level 2 calibration include fuel consumption, tyre wear, vehicle speed and vehicle part usage. The road deterioration and works effects that are calibrated under Level 2 calibration include rutting

progression, initiation of surface distress modes, pavement maintenance results and the environmental impacts. The cost and pricing data from completed projects are also analysed as part of the Level 2 economic calibration process [6]. Level 3 HDM model calibration comprises two elements, improved data collection and fundamental research. Increasing the number of data collection sites over a longer duration of time significantly improves the reliability of input data variables. Traffic data is an example of this, where greater numbers of traffic counters over a longer period yield greater accuracy in the traffic trend and growth factors required for HDM modelling. The fundamental research includes field surveys and experimental studies for the investigation of the functions within the HDM models that are affected by local conditions [6]. Traditional HDM-4 models predict an incremental change in pavement condition parameters with the increase in time. Some observations were made when comparing the HDM-4 models to the measured pavement condition parameters of the New Zealand LTPP programmes. It was found that there was little pavement condition change at the start of the pavement life, but when the deterioration process started, it deteriorated rapidly [7]. An example of the HDM rut depth deterioration model compared to the measured New Zealand rut depth deterioration is presented in Figure 1. The dashed black line represents the default HDM model in the New Zealand DTIMS (Deighton Total Infrastructure Management System), and the solid blue line presents the measured rut depth deterioration. As can be seen from Figure 1, the default HDM model overestimates the measured rut depths of the New Zealand road network. As a result, refinement of the DTIMS pavement deterioration models was initiated to focus on the timing of the pavement rapid deterioration stage. The New Zealand DTIMS models have subsequently moved away from a defined incremental change in condition with respect to time and implemented a probabilistic approach to determine the commencement of the rapid pavement deterioration stage demonstrated in Figure 1. Therefore, it is clear that to refine TII’s PAMS, an LTPP programme of monitoring sites should be selected across the network similar to the refinement methodology of the New Zealand road network. The comparison between the default HDM models should be compared to the measured ground conditions of the LTPP monitoring sites. The suitability of the TII’s PAMS models can subsequently be examined [7].

Figure 1. New Zealand predictive and measured pavement deterioration models [7]

Research Significance

The focus of this study was to conduct a comparative analysis between the predicted pavement deterioration models of TII’s PAMS and the measured pavement condition of the Irish national road network. The research presented here established

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LTPP monitoring sites across the national road network, which were selected from sections of pavement treated between 2010 and 2019. The sections covered all subnet categories, including motorways, dual carriageways, urban areas and single carriageways. The annual national road network survey condition data was geospatially joined to each of the selected LTPP monitoring sites. The pre and post-treatment pavement condition was monitored for each site. Pavement deterioration models were established based on the measured data. A comparative analysis was subsequently carried out between these new models and the predictive pavement deterioration models currently in use.

2 METHODOLOGY

In total, there were 67 LTPP monitoring sites selected for this research. The selected LTPP monitoring sites were segmented with sites split into varying homogeneous segment lengths according to pavement treatment and subnet categories, as outlined by Roberts & Martin [8]. This was adopted to ensure that pavement sections with the same length, similar treatments categories and similar traffic volumes were grouped together for the analysis conducted as part of this research. The number of LTPP monitoring sites segments applicable for the reset value and deterioration modelling analysis is presented in Table 1.

Table 1. Number of LTPP monitoring site segments used in this research

Subnet Category

No. LTPP monitoring site segments

0 3 1 38 2 41 3 78 4 23

Total 183

Since their introduction in 1987, LTPP monitoring sites are still utilised for the optimisation of PAMSs worldwide [4]. This research used LTPP monitoring sites that were maintained or renewed between 2010 to 2019. Selecting LTPP monitoring sites directly after maintenance or renewal ensured that the reset values of the pavement section could be determined, while also tracking the deterioration of the pavement from the start of the pavement’s life. The LTPP monitoring sites were selected from maintenance and renewal sites across the national road network. Although access to data was limited, this research investigated all types of Irish national roads including motorways and dual carriageways (subnet 0), engineered pavements (subnet 1), urban areas (subnet 2) and single carriageways (subnet 3 and 4), and investigates their respective reset values and deterioration trends. The pavement treatment depths were classified into one of four treatments categories based on the TII’s PAMS treatment catalogue [1]. The LTPP pavement treatments are categorised according to the depth of new bituminous material applied to the pavement surface and are presented in Table 2.

Table 2. Four types of LTPP pavement treatment categories

LTPP Pavement Treatment

Depth of new bituminous material applied

Surface Replacement < 50mm Overlay 50 to 100mm

Strengthening 100 to 200mm Reconstruction > 200mm

Assigning the correct type of treatment category to each LTPP monitoring site was an essential element of the LTPP segmentation process. Each pavement treatment was analysed separately to establish the effect that each of the treatment categories have on the pavement condition.

Pavement Reset Value Segmentation

The LTPP monitoring site reset values analysis were segmented by two categories;

1. Treatment category

2. Subnet Type

Segmenting the LTPP monitoring sites according to subnet type and treatment category allowed each monitoring site to be split in accordance with TII’s PAMS. Each segment was assigned a unique identification number, which was used to identify the segment throughout the analysis. These steps were a critical part of the analysis process as they facilitated a direct comparison between the Irish pavement condition parameters and the Irish PAMS’s reset values. The length of LTPP monitoring site segment length applicable for the reset values analysis is presented in Table 3.

Table 3. Pavement reset values analysis lengths

Treatment Category

Length of LTPP monitoring site data (m)

Subnet 0

Subnet 1

Subnet 2

Subnet 3

Subnet 4

Replace Surface

5,720 No

Data 220

No Data

No Data

Overlay No

Data 6,510 5,090 10,100 8,690

Strengthening No

Data 21,560 4,490 25,200 16,860

Reconstruction 94,160 4,640 7,010 10,690 610

Pavement Deterioration Modelling Segmentation

The LTPP monitoring site pavement deterioration analysis was also segmented using two categories;

1. Subnet Type

2. 100m Sample Units

A different segmentation process was required for the pavement deterioration analysis as the pavement deterioration models do not account for the type of pavement treatment. The unique segments were segmented into 100m LTPP monitoring site sample unit lengths, similar to Pulugurtha, et al., [9] and in accordance to the TII’s PAMS segmentation process [10]. This process ensured that each pavement segment sample unit was the same length. If site segments were not split into 100m

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sample units, long segments would be given the same weight as short segment lengths, which would have potentially skewed the analysis results. The quantity and combined length of LTPP monitoring site segments applicable to the pavement deterioration analysis is presented in Table 4.

Table 4. Pavement deterioration analysis lengths

Subnet Category

No. of 100m Sample Units

Sum of Sample Units Length (m)

0 386 38,600 1 1,536 153,600 2 538 53,800 3 1,681 168,100 4 1,024 102,400

Total 5,165 516,500

3 RESULTS

Pavement reset values

The measured IRI, rut depth and LPV3 measured reset results for each pavement subnet category are presented in Tables 5, 6 and 7, respectively. For ease of comparison, the current IRI, rut depth and LPV3 overlay reset values implemented in TII’s PAMS are presented in brackets beneath their respective measured reset values. TII’s PAMS has a relative reset value implemented for the replace surface treatments and an absolute reset value implemented for the overlay, strengthening and reconstruction treatment categories. The replace surface IRI and LPV3 subnet 0 mean reset value results are similar to the current relative reset value, as presented in Tables 5 and 7. According to the LTPP monitoring sites pavement condition results, the measured IRI and LPV3 results indicate that the PAMS replace surface relative reset values are an appropriate representation of the replace surface treatments carried out on the subnet 0 road network. The measured replace surface rut depth reset values for subnet 0 are different to TII’s PAMS reset values, as can be seen from Table 6. For this case, the PAMS underestimates the rut depth improvement resulting from a replace surface treatment on the subnet 0 road network. As a result, the rut depth reset values do not represent the measured pavement condition parameters. Similarly, the measured replace surface IRI, rut depth and LPV3 subnet 2 values, presented in Table 5, 6 and 7 are different to the PAMS relative reset values. The measured IRI subnet 2 values have a difference of 2.3 m/km, the rut depth has a difference 3.4 mm and the LPV3 has a difference of 7.2 between the measured relative reset values and the current PAMS reset values. Therefore, the PAMS underestimates the pavement improvement resulting from a replace surface treatment on the subnet 2 road network and therefore the relative reset values do not represent the measured pavement condition parameters. It should be noted that the subnet 2 replace surface LTPP monitoring site lengths are significantly less then subnet 0, as presented in Table 3, indicating that the subnet 2 relative reset values are reliant on a smaller section of the national road pavement. With this in mind, the pavement condition parameter results still indicate that the current relative reset values for subnet 2 are inappropriate for use under the Irish pavement

condition parameters, albeit with a small length of LTPP monitoring site data available. The measured IRI, rut depth and LPV3 reset values results for the overlay, strengthening and reconstruction treatment categories presented in Tables 5, 6 and 7 are generally smaller than the current PAMS absolute reset values for subnets 1, 2, 3 and 4. This indicates that the PAMS underestimates the IRI, rut depth and LPV3 pavement improvement resulting from the replace surface, overlay, strengthening and reconstruction treatments. As a result the PAMS reset values do not represent the measured IRI, rut depth and LPV3 pavement condition results for subnet categories 1, 2, 3 and 4. Based on the measured LTPP monitoring site results most reset values should be lowered to represent the measured pavement reset values. Additional LTPP monitoring sites should be obtained for the subnet and treatment categories that do not have an applicable LTPP monitoring site length. Therefore completing the reset value analysis for each of TII’s PAMS reset values categories.

Table 5. IRI pavement reset values

Treatment Subnet

0 Subnet

1 Subnet

2 Subnet

3 Subnet

4 Replace Surface (Relative Reset)

-0.4 (-0.5)*

No Data

-2.8 (-0.5)*

No Data

No Data

Overlay No

Data 1.5

(1.7)* 2.6

(1.7)* 2.0

(2.5)* 2.1

(2.5)*

Strengthening No

Data 1.6

(1.4)* 2.5

(2.0)* 1.9

(2.2)* 2.1

(2.2)*

Reconstruction 0.9

(1.0)* 1.8

(1.4)* 2.7

(2.0)* 1.9

(2.2)* 1.4

(2.2)*

*Values in brackets indicate the current PAMS reset values

Table 6. Rut depth pavement reset values

Treatment Subnet

0 Subnet

1 Subnet

2 Subnet

3 Subnet

4 Replace Surface

-2.9 (-2.0)*

No Data

-5.4 (-2.0)*

No Data

No Data

Overlay No

Data 1.9

(2.0)* 2.1

(2.0)* 1.9

(3.0)* 3.1

(4.0)*

Strengthening No

Data 1.7

(2.0)* 2.0

(3.0)* 1.8

(3.0)* 2.8

(4.0)*

Reconstruction 1.6

(2.0)* 1.3

(2.0)* 2.3

(3.0)* 2.2

(3.0)* 1.8

(4.0)* *Values in brackets indicate the current PAMS reset values

Table 7. LPV3 pavement reset values

Treatment Subnet

0 Subnet

1 Subnet

2 Subnet

3 Subnet

4

Replace Surface

-0.3 (-0.5)*

No Data

-7.7 (-0.5)*

No Data

No Data

Overlay No

Data 0.6

(0.8)* 1.0

(0.8)* 0.9

(1.2)* 0.7

(1.2)*

Strengthening No

Data 0.7

(0.8)* 1.0

(1.2)* 0.9

(1.2)* 0.7

(1.2)*

Reconstruction 0.3

(0.8)* 0.8

(0.8)* 1.1

(1.2)* 0.8

(1.2)* 0.4

(1.2)*

*Values in brackets indicate the current PAMS reset values

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Pavement Deterioration Modelling

The IRI, rut depth and LPV3 pavement deterioration modelling analysis was carried out for subnets 1, 2, 3 and 4. The IRI, rut depth and LPV3 pavement deterioration models for subnet 1 are presented in Figures 2, 3 and 4, respectively. The IRI, rut depth and LPV3 pavement deterioration models for subnet 2, 3 and 4 are not presented in this paper as they demonstrate similar results to that of the presented subnet 1 results in Figures 2, 3 and 4. Figures 2, 3 and 4 present the measured pavement condition values for each LTPP 100m sample unit, and the data points represent the pavement condition parameters of each 100m sample unit as the cumulative traffic, in the form of equivalent standard axle loads (ESALS), increases across the LTPP monitoring site. The closer the LTPP monitoring site treatment date is to 2010, the greater the amount of IRI, rut depth and LPV3 pavement condition data applicable to each monitoring site. The red dashed line represents the deterioration models for the predicted PAMS IRI, rut depth and LPV3 pavement condition parameters. The black solid lines illustrate the line of best fit according to the measured pavement condition parameters as analysed as part of this research. The models are presented on the same graphs in Figures 2, 3 and 4 to facilitate direct comparison between the theoretical PAMS model results and the measured values of the parameters. As can be seen from Figure 2, the IRI theoretical PAMS model and the measured pavement conditions do not closely align, with the PAMS theoretical model indicating a more significant IRI deterioration as traffic volume increases. This trend indicates that the theoretical PAMS deterioration models are overestimating the effect traffic has on the IRI pavement condition deterioration. For the most part, the best fit line for the measured values presented in Figure 2 indicates that the IRI ground conditions values for subnet 1 exhibit only minor deterioration throughout the monitoring period. As a result, the rapid increase in pavement IRI results and subsequent increase in slope, experienced during New Zealand’s LTPP programme as discussed in the introduction, is not apparent in any of the actual IRI pavement condition deterioration models. This indicates that the rapid deterioration stage of the IRI pavement condition has not yet occurred for the selected LTPP monitoring sites. This indicates that the analysis presented here should be extended into the future to fully determine and complete the IRI deterioration modelling process over a longer timeframe.

Figure 2. IRI subnet 1 deterioration models

The rut-depth pavement deterioration models for subnet 1 are presented in Figure 3. It is clear that the PAMS model and the measured pavement condition best fit model do not align. The PAMS deterioration model commences the pavement rut-depth deterioration process at 0mm, whereas the measured pavement condition model commences at 1.6mm. As the cumulative traffic increases, the PAMS theoretical rut depth pavement deterioration rapidly increases and subsequently flattens. The rut-depth measured pavement condition model exhibits a gradual increase in rut depth values with an increase in cumulative traffic. These deterioration models indicate TII’s PAMS theoretical models underestimate the measured rut-depth pavement condition values at the start of the pavement life, and subsequently overestimates the rut depth condition values once the traffic volumes cumulate above 1.75 million ESALs. For the most part, the solid line of best fit in Figure 3 indicates that the measured rut-depth condition values for subnet 1 exhibit only minor deteriorations over the monitoring period. As a result, the rapid increase in pavement rut depth results and subsequent increase in slope, experienced during New Zealand’s LTPP programme as discussed in the introduction, is not apparent in any of the measured rut-depth pavement condition deterioration models. This indicates that the rapid deterioration stage of the rut depth pavement condition has not yet occurred for the selected LTPP monitoring sites. Therefore, the timeframe of the analysis should be extended to include the effects of future pavement treatments to fully determine and complete the rut depth deterioration modelling process. The LPV3 pavement deterioration models for subnet 1 are presented in Figure 4. The PAMS theoretical model and the measured pavement condition model do not align. The PAMS deterioration model commences the LPV3 pavement deterioration modelling at 0.5, whereas the measured pavement condition deterioration model commences at 0.9. As the cumulative traffic increases, the PAMS theoretical LPV3 pavement deterioration model increases linearly with a greater slope than that of the measured model. The measured LPV3 pavement deterioration model exhibits a gradual increase in LPV3 values with an increase in cumulative traffic. These deterioration models indicate that the PAMS theoretical deterioration model underestimates the LPV3 pavement condition values for cumulative traffic less than 0.7 million ESALs and overestimates the LPV3 pavement condition values for cumulative traffic above 0.7 million ESALs. For the most part, the measured pavement model in Figure 4 indicates the measured LPV3 ground condition values for all subnets exhibit only minor deterioration throughout the monitoring period. As a result, the rapid increase in pavement LPV3 results and subsequent increase in slope, experienced during the New Zealand’s LTPP programme as discussed in the introduction, is not observed in any of the measured LPV3 pavement deterioration models as part of this research. This indicates that the rapid deterioration stage of the LPV3 pavement condition has not yet occurred for the selected LTPP monitoring sites. This analysis should be extended to fully determine and complete the LPV3 deterioration modelling process.

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Figure 3. Rut depth subnet 1 deterioration models

Figure 4. LPV3 subnet 1 deterioration models

4 CONCLUSIONS

Pavement Reset Values

Generally, the reset values in use in the PAMS underestimate the level of improvement obtained from the applied treatments observed in this study, based on the average post-works condition. There are numerous reasons why this difference may be observed. The initial PAMS reset values may have been overly conservative or improvements in construction practices over the last decade may actually be producing better results on site. On the other hand, the treatments as categorised in this study may not map directly to the PAMS treatment catalogue e.g. a treatment recorded as an surface replacement may have also included significant regulation or spot repairs prior to the surface replacement taking place, and so might more appropriately be compared to a structural overlay. Finally, average condition may not be the best way to assess the post-works. An 80th or 85th percentile may be more appropriate and more similar to the existing resets. In light of the differences observed it is suggested that a comprehensive review of post-works condition be implemented, with the aim of improving the accuracy of the reset values used in the PAMS. The data used in this study will form a very good basis for this but should be supplemented with additional data where available. In particular, the Subnet 2 Replace Surface and Subnet 4 Reconstruction datasets would benefit from significant expansion. Additionally, this research recommends that GPR surveys should be performed on all LTPP monitoring sites before and after the pavement treatments are carried out. Conducting before and after GPR

surveys accurately determines the depth of new bituminous material applied allowing more accurate treatment classification.

Pavement Deterioration Models

The measured rate of pavement deterioration for the observed traffic volumes is generally lower than the rates used in the PAMS deterioration models, based on the pavement condition data from 2010 to 2019. As with the reset values, the lower rates of pavement deterioration may be a result of the improving pavement construction practices on the Irish national road network. The research recommends that a review of the PAMS pavement deterioration models should be undertaken, using the data compiled for this study as a basis for a more comprehensive deterioration modelling study. Probabilistic modelling would be a useful addition to the current deterministic models, particularly on low volume non-engineered roads where age and weather effects may be more influential than traffic loads.

General

The current PAMS reset values and deterioration models were originally configured based primarily on engineering judgement and international best practice. At the time there was insufficient documentation on the location, type and effect of treatments carried out prior to the implementation of a PAMS to use empirical methods to determine appropriate models. Use of the PAMS has enabled an extensive archive of condition data to be created. This, together with better documentation on rehabilitation works enables both the reset values and the deterioration models to be recalibrated in line with the now available data. More accurate deterioration models and reset values offer immediate improvements to the Benefit/Cost calculations used in the LCCA providing a more optimal use of available pavement budgets.

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Management Services. [2] McKibbins, L., Spink, T. & Power, C., 2019. Deterioration modelling of

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[5] Pradham, N. & Mallela, R., 2002. Comparison of Predictive Pavement Management Models (HDM-III, HDM-4, NZ dTIMS) for New Zealand Conditions, Wellington: Transfund New Zealand Research Report No. 227.

[6] Bennett, C. R. & Paterson, W. D. O., 2000. A Guide to Calibration and Adaption Volume 5, Paris: The World Road Association.

[7] Hatcher, W. E. & Henning, T. F., 2008. Lessons Learnt: New Zealand National Pavement Performace Model Implementation. s.l., 7th International Conference on Managing Pavement Assets.

[8] Roberts, J. & Martin, T., 1996. Recommendatnios for monitoring pavement performace, Vermount South: Australian Road Research Board Report.

[9] Pulugurtha, S. S. et al., 2013. Preliminary Results towards Developing Thresholds for Pavement Condition Maintenance: Safety Perspective. Procedia - Social and Behaviour Sciences, Volume 104, pp. 302-311.

[10] National Roads Authority, 2015. HD31: Pavement Assessment, Repair and Renewal Principles. Dublin: National Roads Authority.

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