Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Civil Engineering


Civil Engineering

First Advisor

Mohammad Najafi


Sanitary sewer pipes infrastructure system in good condition is essential in 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 dissertation 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' data. The MLR model is built using 80% of randomly selected data and validated using the remaining 20% of data. Similarly, the ANN model is trained, validated, and tested. The results of this research reveal that MLR and ANN models are acceptable. The significant physical factors influencing sanitary pipes condition rating include diameter, age, pipe material, and segment length. Soil type is the most important 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 dissertation 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.


Prediction model, Sanitary sewer pipes, Condition assessment, Multinomial logistic regression, Artificial neural network


Civil and Environmental Engineering | Civil Engineering | Engineering


Degree granted by The University of Texas at Arlington