New Multiobjective Optimization Approach to Rehabilitate and Maintain Sewer Networks Based on Whole Lifecycle Behavior

Abstract

This study proposes a new methodology for selecting renewal plans for sewer networks based on their impacts on the behavior of the networks over their whole lifecycle. The proposed approach combines a multiobjective genetic algorithm and Monte Carlo simulation to maximize network condition and serviceability while minimizing network risk of failure and total lifecycle cost for the entire planning period. The algorithm was applied to a sewer network in Sahab City, Jordan, in 16 different analysis scenarios that consider the uncertainty in the model variables. These different analysis scenarios varied the network age, deterioration rate, and available budget at each time step throughout the planning period. The model was then validated by statistically comparing its performance to an existing prioritization model that does not consider the long-term impact behavior of the wastewater system. The results show that the proposed algorithm outperforms the existing prioritization model because it results in statistically significant improvement in the network condition, risk of failure, serviceability, and the total lifecycle cost at the end of the planning period.