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

Author

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  • 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)

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.

Suggested Citation

  • Daniel Ogaro Atambo & Mohammad Najafi & Vinayak Kaushal, 2022. "Development and Comparison of Prediction Models for Sanitary Sewer Pipes Condition Assessment Using Multinomial Logistic Regression and Artificial Neural Network," Sustainability, MDPI, vol. 14(9), pages 1-20, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5549-:d:808883
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    References listed on IDEAS

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    1. Angeliki Peponi & Paulo Morgado & Jorge Trindade, 2019. "Combining Artificial Neural Networks and GIS Fundamentals for Coastal Erosion Prediction Modeling," Sustainability, MDPI, vol. 11(4), pages 1-14, February.
    2. Peng Hou & Xiaojian Yi & Haiping Dong, 2020. "A Spatial Statistic Based Risk Assessment Approach to Prioritize the Pipeline Inspection of the Pipeline Network," Energies, MDPI, vol. 13(3), pages 1-16, February.
    3. Stian Bruaset & Håkon Rygg & Sveinung Sægrov, 2018. "Reviewing the Long-Term Sustainability of Urban Water System Rehabilitation Strategies with an Alternative Approach," Sustainability, MDPI, vol. 10(6), pages 1-30, June.
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    Cited by:

    1. Yilin Zhao & Feng He & Ying Feng, 2022. "Research on the Current Situation of Employment Mobility and Retention Rate Predictions of “Double First-Class” University Graduates Based on the Random Forest and BP Neural Network Models," Sustainability, MDPI, vol. 14(14), pages 1-22, July.
    2. Xuming Zeng & Zinan Wang & Hao Wang & Shengyan Zhu & Shaofeng Chen, 2023. "Progress in Drainage Pipeline Condition Assessment and Deterioration Prediction Models," Sustainability, MDPI, vol. 15(4), pages 1-29, February.

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