New Approach for Critical Pipe Prioritization in Wastewater Asset Management Planning

Abstract

This study proposes a new methodology that enables utilities to focus on the most critical pipes in the network while maximizing the network performance. Two hybrid algorithms were proposed for this purpose; the first algorithm combines dynamic programming with Monte Carlo simulation, and the second algorithm combines a single-objective ant colony with Monte Carlo simulation. The proposed algorithms were applied to a sewer network in Sahab City, Jordan, in 24 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 two algorithms were then validated by statistically comparing their performance with that of a present prioritization model that does not prefer the critical pipes in the network while maximizing the network performance. The results show that the proposed algorithms outperform that present prioritization model because of its ability to select the most critical pipes in the network and at the same time it results in a better network condition, lower risk of failure, and better network serviceability within the available budget at each time step.