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Analysis of the pavement deterioration uncertainty scenarios on pavement maintenance and rehabilitation planning optimization
This study aims to compare the results of stochastic and deterministic models and their corresponding optimal M&R actions considering different budget and deterioration rate uncertainty scenarios. Moreover, the impacts of uncertainty on network’s scale are investigated. Therefore, this study applies Multi-Stage stochastic programming to model the uncertainty of these parameters in pavement M&R optimization. A new approach is proposed to investigate the effects of different uncertainty cases on the mentioned problem. Moreover, two pavement networks, including a large-scale and a small-scale, are utilized to evaluate the role of network size in the optimal solution to the pavement M&R optimization in the uncertainty conditions. Due to the high complexity of the large-scale M&R problem, Progressive Hedging Algorithm as an effective decomposition technique is applied. Three different uncertainty cases, including low, medium, and high are considered for the deterioration rate. Furthermore, two scenarios are taken into account for the budget: low reduction and high reduction. The results show that the probability of selecting preventive maintenance in the optimal M&R plan is increased by increasing the severity of uncertainty cases. Therefore, preventive maintenance is the most effective pavement treatment to reduce the adverse effects of budget and pavement deterioration uncertainty.
Analysis of the pavement deterioration uncertainty scenarios on pavement maintenance and rehabilitation planning optimization
This study aims to compare the results of stochastic and deterministic models and their corresponding optimal M&R actions considering different budget and deterioration rate uncertainty scenarios. Moreover, the impacts of uncertainty on network’s scale are investigated. Therefore, this study applies Multi-Stage stochastic programming to model the uncertainty of these parameters in pavement M&R optimization. A new approach is proposed to investigate the effects of different uncertainty cases on the mentioned problem. Moreover, two pavement networks, including a large-scale and a small-scale, are utilized to evaluate the role of network size in the optimal solution to the pavement M&R optimization in the uncertainty conditions. Due to the high complexity of the large-scale M&R problem, Progressive Hedging Algorithm as an effective decomposition technique is applied. Three different uncertainty cases, including low, medium, and high are considered for the deterioration rate. Furthermore, two scenarios are taken into account for the budget: low reduction and high reduction. The results show that the probability of selecting preventive maintenance in the optimal M&R plan is increased by increasing the severity of uncertainty cases. Therefore, preventive maintenance is the most effective pavement treatment to reduce the adverse effects of budget and pavement deterioration uncertainty.
Analysis of the pavement deterioration uncertainty scenarios on pavement maintenance and rehabilitation planning optimization
Fani, Amirhossein (author) / Golroo, Amir (author) / Fahmani, Mohammadsadegh (author) / Naseri, Hamed (author) / Moghadas Nejad, Fereidoon (author)
Structure and Infrastructure Engineering ; 21 ; 507-524
2025-03-04
18 pages
Article (Journal)
Electronic Resource
English
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