Development and Comparison of Prediction Models for Sanitary Sewer Pipes Condition Assessment Using Multinomial Logistic Regression and Artificial Neural Network

Daniel Ogaro Atambo, Mohammad Najafi and Vinayak Kaushal
Additional contact information
Daniel Ogaro Atambo: Center for Underground Infrastructure Research and Education (CUIRE), Department of Civil Engineering, The University of Texas at Arlington, P.O. Box 19308, Arlington, TX 76019, USA
Mohammad Najafi: Center for Underground Infrastructure Research and Education (CUIRE), Department of Civil Engineering, The University of Texas at Arlington, P.O. Box 19308, Arlington, TX 76019, USA
Vinayak Kaushal: Center for Underground Infrastructure Research and Education (CUIRE), Department of Civil Engineering, The University of Texas at Arlington, P.O. Box 19308, Arlington, TX 76019, USA

Sustainability, 2022, vol. 14, issue 9, 1-20

Abstract: Sanitary sewer pipes infrastructure system being in good condition is essential for providing safe conveyance of the wastewater from homes, businesses, and industries to the wastewater treatment plants. For sanitary sewer pipes to deliver the wastewater to the treatment plants, they must be in good condition. Most of the water utilities have aged sanitary sewer pipes. Water utilities inspect sewer pipes to decide which segments of the sanitary sewer pipes need rehabilitation or replacement. The process of inspecting the sewer pipes is described as condition assessment. This condition assessment process is costly and necessitates developing a model that predicts the condition rating of sanitary sewer pipes. The objective of this study is to develop Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) models to predict sanitary sewer pipes condition rating using inspection and condition assessment data. MLR and ANN models are developed from the City of Dallas’s data. The MLR model is built using 80% of randomly selected data and validated using the remaining 20% of data. The ANN model is trained, validated, and tested. The significant physical factors influencing sanitary pipes condition rating include diameter, age, pipe material, and length. Soil type is the environmental factor that influences sanitary sewer pipes condition rating. The accuracy of the performance of the MLR and ANN is found to be 75% and 85%, respectively. This study contributes to the body of knowledge by developing models to predict sanitary sewer pipes condition rating that enables policymakers and sanitary sewer utilities managers to prioritize the sanitary sewer pipes to be rehabilitated and/or replaced.

Keywords: prediction model; sanitary sewer; condition assessment; multinomial logistic regression; artificial neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/9/5549/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/9/5549/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:9:p:5549-:d:808883

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().