Prioritization Model for Road Pavement Maintenance Fund Allocation Considering Cross Assets

<p dir="ltr">A well-maintained transportation infrastructure system dictates the hallmark of a well-functioning economy. Roads are the backbone of land transportation, sustaining the socio-economic connection from urban economic hubs to rural farmlands. Due to the huge demand for road assets, their condition deteriorates mainly because of ongoing traffic loads and environmental factors. The condition of the road asset is highly dependent on other road-related assets such as bridges and water pipes, and therefore, they are often considered together in a cross-asset system. Thus, maintenance activities on one asset can enhance the degradation of other assets in the system. The present study considers road pavements and water pipes as the two assets in a cross-asset system. In the traditional approach, the maintenance of these two assets is planned individually without considering the degradation of the other. This enhances maintenance induced-asset deterioration on the cross-asset system and results in frequent asset interventions leading to extra burden on the economy of the country. Since there is a constrained budget allocation for the maintenance of these two assets infrastructure, an integrated framework for cross-asset maintenance is required to utilize the available limited budgets optimally. Therefore, main aim of this research was to develop a prioritization framework to support fund allocation decisions of road asset managers considering an integrated approach of cross assets (roads pavement and water pipes) using collected monitoring data, calibrated degradation modeling tools, and expert surveys. </p><p dir="ltr">Phase one of the research was planned to identify the critical assets for the study of cross-asset systems based on the discussions with the asset management authorities and published literature. Accordingly, water pipe asset was identified as the most influential cross asset for the maintenance of the road pavement asset. </p><p dir="ltr">In the first part of Phase 2 of this study, it was aimed to develop degradation prediction models for unsealed roads based on the field data and to identify the optimum intervention strategy for unsealed roads. The field data collections were conducted over a period of 12 months, selecting two unsealed roads in Sri Lanaka to derive the gravel loss (GL) and unpaved road condition index (UPCI) parameters. The field observed GL values were compared with available deterministic models. Then, Markov-based probabilistic degradation prediction was conducted for GL and UPCI indices followed by a cost prediction and a life-cycle prediction. The results revealed that the probabilistic Markov method can be more appropriate to model the GL and UPCI deterioration of unsealed roads than the deterministic models. Furthermore, the UPCI-based method was identified to be more reliable for condition assessment, cost prediction and life cycle predictions on unsealed roads. A 5-years intervention period without allowing the road to reach the failure condition was identified as the optimum intervention strategy for unsealed roads. </p><p dir="ltr">In the second part of Phase 2 of this study, degradation prediction models were developed for sealed roads and optimum maintenance strategies were identified. The analysis was based on a 15-year historical pavement condition survey data collected over a portion of the Australian national road network, selecting the international roughness index (IRI) as the critical parameter. This involved classification of road data into homogeneous categories based on the pavement maintenance category (PMC), region (urban or rural) and surface material (asphalt or spray seal), evaluation of the effect of different environmental factors on IRI distribution and developing Markov-based probabilistic degradation prediction and life cycle analysis. The outcomes of the study revealed that spray seal roads exhibited high deterioration compared to Asphalt surfaces. The ‘regulation + resurface’ maintenance at trigger condition level 4 was identified as the optimum intervention for sealed roads. </p><p dir="ltr">The third phase of the study aimed to develop an optimum fund allocation strategy for a cross-asset system by combining the maintenance of the road asset and the water pipe asset. The Analytic Hierarchy Process (AHP) was used for the prioritization of the asset groups (roads and water pipes), asset elements (within each group), and maintenance activities, considering the critical criteria for the maintenance. Inputs for these AHP developments were collected through two questionnaire surveys conducted among the asset management experts in the field of road asset maintenance and water pipe asset maintenance. This study was followed by two case studies in Sri Lanka and Australia, and the optimum combinations of the intervention levels for each element in the asset system were identified, considering the available budgets. A trade-off analysis was conducted in these case studies, and the optimum maintenance strategy for the integrated multiple-asset system was determined by comparing the cost values and the benefit (condition improvement) to cost ratio. From these studies, it was revealed that the fund allocation ratio among roads and pipe assets in a cross-asset project is 0.7:0.3 in Sri Lanka and 0.58: 0.42 in Australia. </p><p dir="ltr">Phase 4 of this research was designed to develop a framework for the maintenance prioritization of cross-asset projects falling under the same budget. In this framework, the ranking was decided based on the priority value ‘Overall asset maintenance rating’ (OAMR), which is a measure of the overall maintenance requirement of the considered asset system. OAMR was calculated by combining the asset maintenance ratings (AMR) of each individual asset group (road and water pipe asset groups) and it represents the average rating value for the entire project corridor. AMR value combines the performance indicators related to asset condition and asset value (including risk analyses) for each asset group. Overall, the framework assessed the actual maintenance priority of the project considering the physical conditions and financial risks associated with the assets in the project. The proposed method was applied to a case study in Australia, and the selected maintenance projects were ranked based on the OAMR values. The findings of this study could be applied to intelligent asset maintenance models to evaluate the absolute maintenance requirement of a project and to compare it with the other projects for the prioritization of the fund allocations. Overall, this research identifies optimum maintenance strategies for unsealed and sealed road pavements through probabilistic degradation prediction and life cycle analysis. Based on the optimum maintenance strategies for individual assets, road assets and water pipe assets were integrated for maintenance planning to achieve the optimum utilization of limited maintenance budgets. Further the OAMR rating system facilitates the prioritization of road related cross asse projects by combining the asset condition-based performance indicators with the asset value-based performance indicators. The outcomes and the developed framework could be implemented in asset management platforms to assist in decision making related to road asset maintenance considering water pipes as a critical cross asset.</p>