An efficient multi-objective optimization framework to maintain road networks based on Bayesian optimization and reliability theory

Abstract

This study proposes an efficient asset management framework that enables decision makers to prioritize the maintenance of their roads, while focusing on the most critical road segments. In particular, this study first extends the application of reliability theory to estimate the overall network condition. Following that, this study proposes a new consequence of failure function for the whole road network based on road segments’ reliability, age, and road agency preferences. Finally, the study proposes an efficient multi-objective optimization algorithm, with the goal of maximizing overall network performance with the least maintenance and computational cost. The suggested framework was applied to three main roads in Jordan and validated statistically by comparing its performance to that of a typical multi-objective genetic algorithm (GA) under various scenarios and utilizing multiple performance metrics. The results show that the proposed algorithm outperforms the traditional GA in terms of objective function values, convergence, and solution spread.