Systematic Development\nof a Machine Learning-Based\nAsset Management Tool for Wastewater Pipeline Networks

Utility companies face significant challenges in managing\nwastewater\ndistribution networks (WWDN), where continuous service delivery is\ncrucial. The American Society of Civil Engineers (ASCE) rated the\nU.S. wastewater treatment infrastructure as inadequate, assigning\nit a D+ grade due to its high risk of failure. Such failures can lead\nto severe economic and environmental damage, with untreated waste\ncontaminating ecosystems and causing costly cleanups. Traditionally,\nthe industry has relied on reactive, subjective asset management,\naddressing issues only after failures occur. This reactive approach\noften results in unexpected expenses and strains operational budgets.\nTo address these challenges, we propose a proactive, data-driven asset\nmanagement framework for the wastewater industry. Our strategy aims\nto reduce unforeseen costs by minimizing the likelihood of asset failure,\nenvironmental risks, and financial losses. By leveraging machine learning,\nspecifically random forest classification, and analyzing historical\ndata, we developed a predictive tool in Python. This tool identifies\nhigh-risk assets, enabling prioritized maintenance actions, ultimately\nmitigating potential environmental impacts and associated costs.