ORCID Identifier(s)


Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Civil Engineering


Civil Engineering

First Advisor

Ali Abolmaali

Second Advisor

Mohsen Shahandashti


ABSTRACT: ARTIFICIAL INTELLIGENCE-BASED EVALUATION OF PIPELINE STRUCTURES USING MULTI-SENSOR ROBOTS Mohammad Shaher Rababeh, Ph.D. The University of Texas at Arlington, August 2023 Supervising Professor: Ali Abolmaali All around the world, pipeline systems are safe, efficient, and cost-effective for transporting liquids such as stormwater and wastewater. However, different defects develop in pipeline structures with aging and use. Leaks and significant failures might create severe danger for the environment, and for humans in urban areas. This deterioration in pipelines happens under the influence of different factors. In general, observing and evaluating pipelines will allow us to understand its behavior under various conditions and how they are affected. The most common way to assess and evaluate pipeline structures are inspections which can be performed utilizing different types of equipment and methods depending on the site condition and type of data desired or needed to be collected, major types of the inspections and its methods will be presented and discussed throughout this dissertation. The main multi sensor robotic boat was utilized in the inspections performed to collect the data set of this dissertation is the multi-sensor robotic boat designed and built by center of structural engineering simulation and pipeline inspection (CSER-PI) at the University of Texas at Arlington. Several inspections were performed of sewer lines for the City of Mansfield and Trinity River authority the material of the inspected pipes were mainly reinforced concrete pipes (RCP), this collected data of approximately [7] miles formed the data set utilized for the research purpose of this dissertation. One of the most significant problems in concrete pipes is corrosion due to the H2S presence in the sewer pipe, corrosion in some of the cases such as the case of this dissertation can be the main factor causing the degradation of the pipe and pushing the pipe towards failure or the end of useful service life. In this dissertation multiple types of models were constructed and utilized to analyze the data set and ultimately predict the corrosion rate in concrete pipe structures under various parameters.


Artificial intelligence, Pipeline structures, Multi sensores, Pipelines inspection, Prediction models, Concrete pipelines, Concrete corrosion


Civil and Environmental Engineering | Civil Engineering | Engineering


Degree granted by The University of Texas at Arlington

Available for download on Friday, August 01, 2025