ORCID Identifier(s)


Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Civil Engineering


Civil Engineering

First Advisor



Underground utilities and wastewater collection systems deteriorate over time demanding utility owners to involve in continuous revisions and development of their asset management frameworks to maintain the functionality of their assets. In any asset management framework, inspection of an asset and respective condition assessment plays a vital role in successful operation and maintenance of systems. In the United States, closed-circuit television (CCTV) is the commonly used device for inspecting the inner environment of sewer pipes, which considering the large length of pipe inventory in a city, is a relatively expensive and time-consuming process. Therefore, inspection of every individual sanitary sewer pipe segment is not feasible in a short time period for any municipality owing to their large inventory of these pipes. However, sanitary sewer pipe segments in need of repair or a maintenance activity can be prioritized in advance for inspection based on their historical performance. Therefore, the primary objective of this dissertation is to develop a sanitary sewer pipe condition prediction model. Data collected from City of Fort Worth, Texas, is utilized in model development. Various supervised machine learning algorithms such as logistic regression (LR), k-nearest neighbors (k-NN) and random forests (RF) are employed. Numerous evaluation metrics such as precision, recall, F1-score and area under curve (AUC) are estimated to compare the performance of developed models. Resulted F1-score for the RF model is 0.94 while LR and k-NN models resulted 0.83 and 0.44, respectively. The results show that random forests model performed better than both LR and k-NN models. As a secondary objective of this dissertation, a decision support tool was developed for asset managers to utilize above models during inspection phase to estimate condition of their sanitary sewers for identification of critical sewers in need of immediate attention.


Condition Assessment, Inspection


Civil and Environmental Engineering | Civil Engineering | Engineering


Degree granted by The University of Texas at Arlington