ORCID Identifier(s)

0000-0002-4508-7045

Graduation Semester and Year

2021

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Civil Engineering

Department

Civil Engineering

First Advisor

Unknown

Abstract

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.

Keywords

Condition Assessment, Inspection

Disciplines

Civil and Environmental Engineering | Civil Engineering | Engineering

Comments

Degree granted by The University of Texas at Arlington

30023-2.zip (3229 kB)

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.