A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Systematic Development of a Machine Learning-Based Asset Management Tool for Wastewater Pipeline Networks
https://orcid.org/0000-0002-6090-527X
https://orcid.org/0000-0002-8044-0369
Utility companies face significant challenges in managing wastewater distribution networks (WWDN), where continuous service delivery is crucial. The American Society of Civil Engineers (ASCE) rated the U.S. wastewater treatment infrastructure as inadequate, assigning it a D+ grade due to its high risk of failure. Such failures can lead to severe economic and environmental damage, with untreated waste contaminating ecosystems and causing costly cleanups. Traditionally, the industry has relied on reactive, subjective asset management, addressing issues only after failures occur. This reactive approach often results in unexpected expenses and strains operational budgets. To address these challenges, we propose a proactive, data-driven asset management framework for the wastewater industry. Our strategy aims to reduce unforeseen costs by minimizing the likelihood of asset failure, environmental risks, and financial losses. By leveraging machine learning, specifically random forest classification, and analyzing historical data, we developed a predictive tool in Python. This tool identifies high-risk assets, enabling prioritized maintenance actions, ultimately mitigating potential environmental impacts and associated costs.
This research presents a proactive asset management framework using machine learning to enhance the reliability and reduce costs in wastewater treatment networks.
Systematic Development of a Machine Learning-Based Asset Management Tool for Wastewater Pipeline Networks
https://orcid.org/0000-0002-6090-527X
https://orcid.org/0000-0002-8044-0369
Utility companies face significant challenges in managing wastewater distribution networks (WWDN), where continuous service delivery is crucial. The American Society of Civil Engineers (ASCE) rated the U.S. wastewater treatment infrastructure as inadequate, assigning it a D+ grade due to its high risk of failure. Such failures can lead to severe economic and environmental damage, with untreated waste contaminating ecosystems and causing costly cleanups. Traditionally, the industry has relied on reactive, subjective asset management, addressing issues only after failures occur. This reactive approach often results in unexpected expenses and strains operational budgets. To address these challenges, we propose a proactive, data-driven asset management framework for the wastewater industry. Our strategy aims to reduce unforeseen costs by minimizing the likelihood of asset failure, environmental risks, and financial losses. By leveraging machine learning, specifically random forest classification, and analyzing historical data, we developed a predictive tool in Python. This tool identifies high-risk assets, enabling prioritized maintenance actions, ultimately mitigating potential environmental impacts and associated costs.
This research presents a proactive asset management framework using machine learning to enhance the reliability and reduce costs in wastewater treatment networks.
Systematic Development of a Machine Learning-Based Asset Management Tool for Wastewater Pipeline Networks
https://orcid.org/0000-0002-6090-527X
https://orcid.org/0000-0002-8044-0369
Utility companies face significant challenges in managing wastewater distribution networks (WWDN), where continuous service delivery is crucial. The American Society of Civil Engineers (ASCE) rated the U.S. wastewater treatment infrastructure as inadequate, assigning it a D+ grade due to its high risk of failure. Such failures can lead to severe economic and environmental damage, with untreated waste contaminating ecosystems and causing costly cleanups. Traditionally, the industry has relied on reactive, subjective asset management, addressing issues only after failures occur. This reactive approach often results in unexpected expenses and strains operational budgets. To address these challenges, we propose a proactive, data-driven asset management framework for the wastewater industry. Our strategy aims to reduce unforeseen costs by minimizing the likelihood of asset failure, environmental risks, and financial losses. By leveraging machine learning, specifically random forest classification, and analyzing historical data, we developed a predictive tool in Python. This tool identifies high-risk assets, enabling prioritized maintenance actions, ultimately mitigating potential environmental impacts and associated costs.
This research presents a proactive asset management framework using machine learning to enhance the reliability and reduce costs in wastewater treatment networks.
Systematic Development of a Machine Learning-Based Asset Management Tool for Wastewater Pipeline Networks
Stengel, Jake (author) / Aboagye, Emmanuel (author) / Le, Phuong (author) / DeNafo, Matt (author) / Snyder, Dylan (author) / Nelson, Nathanial (author) / Yenkie, Kirti (author)
ACS ES&T Water ; 4 ; 5555-5565
2024-12-13
Article (Journal)
Electronic Resource
English
Asset Management for Urban Wastewater Pipeline Networks
| Online Contents | 2010
Little Rock Wastewater Asset Valuation and Asset Management Development Project
| British Library Conference Proceedings | 2009
Development and implementation of an asset management framework for wastewater collection networks
| British Library Online Contents | 2014
Development and implementation of an asset management framework for wastewater collection networks
| Online Contents | 2014