Abstract
Addressing the multi-dimensional challenges to promote pavement sustainability requires the development of an optimization approach by simultaneously taking into account future pavement conditions for pavement maintenance with the capability to search and determine optimal pavement maintenance strategies. Thus, this research presents an integrated approach based on the Markov chain and Particle swarm optimization algorithm which aims to consider the predicted pavement condition and optimize the pavement maintenance strategies during operation when applied in the maintenance management of a road pavement section. A case study is conducted for testing the capability of the proposed integrated approach based on two maintenance perspectives. For case 1, maintenance activities mainly occur in TM20, TM31, and TM41, with the maximum maintenance mileage reaching 88.49 miles, 50.89 miles, and 20.91 miles, respectively. For case 2, the largest annual maintenance cost in the first year is $15.16 million with four types of maintenance activities. Thereafter, the maintenance activities are performed at TM10, TM31, and TM41, respectively. The results obtained, compared with the linear program, show the integrated approach is effective and reliable for determining the maintenance strategy that can be employed to promote pavement sustainability.
Highlights
The condition of road pavement is vulnerable to the impact of uncertain environmental factors and traffic loads, resulting in pavement deterioration over time (Chou & Le, 2011; Elhadidy et al, 2015; Neal & Pro, 2020; Pantuso et al, 2019)
The suitable model constructed in this research includes the following steps: (a) identification of decision variables; the decision variables are designed to represent all feasible maintenance activities to be performed in each pavement state per year; (b) establishment of the objective function; the objective function is an important part of the optimal approach and is of interest to stakeholders; (c) establishment of constraint set; the constraint set to be considered is to ensure the solution satisfying the requirements of logic, road pavement miles, road pavement quality condition, and budget limit
The Markov chain (MC) is a commonly used probabilistic model for pavement performance prediction, which treats pavement condition indicators as random variables and can explain the uncertainty associated with pavement deterioration
Summary
The condition of road pavement is vulnerable to the impact of uncertain environmental factors and traffic loads, resulting in pavement deterioration over time (Chou & Le, 2011; Elhadidy et al, 2015; Neal & Pro, 2020; Pantuso et al, 2019). Ji et al An integrated multi-objectives optimization approach on modelling pavement maintenance strategies These models leveraged for predicting future pavement conditions is effective, its applicability to road pavement deterioration has not been fully validated due to the small number of cases. Existing methods applied the MC to predict future pavement conditions by the state transition probability matrix (TPM) (Pérez-Acebo et al, 2018; Saha et al, 2017; Tabatabaee & Ziyadi, 2013) Those include the research conducted by Mandiartha et al (2017), which applied the MC to model the road pavement deterioration process. This research, presents an integrated approach to determine maintenance strategies for stakeholders to promote pavement sustainability in road maintenance management with MC and an efficient optimization algorithm considering several constraints, such as limited maintenance budget and overall high-quality conditions.
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