Asset Management of Wastewater Interceptors Adjacent to Bodies of Water

Abstract

Pipeline asset management derives from pipelines’ physical conditions, condition rating, and serviceability through investigating, monitoring, and analyzing the rupture history. The remaining asset life and structural condition of the pipeline network running near and under bodies of water are often hard to predict. In case of a pipeline failure, major damage may occur to the surrounding environment, adding up to disruptions in service and repair costs. This paper develops multinomial logistic regression (MLR) and binary logistic regression models to predict how the bodies of water could affect the soil surrounding wastewater interceptors. The models were developed based on data from the City of Fort Worth, Texas. This study concludes that the pipe diameter, pipe age, location of the pipeline with reference to bodies of water (far or near), and the pipe material are the most significant variables that affect the surrounding conditions and remaining life of wastewater interceptors. In future, a clearer perception through increased software development and machine learning for managing pipeline asset management would provide impacts on different parameters on pipelines’ expected life.

Keywords

• asset management
• wastewater
• artificial intelligence
• pipelines
• multinomial logistic regression
• binary logistic regression