ORCID Identifier(s)


Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Civil Engineering


Civil Engineering

First Advisor

Mohammad Najafi


According to U.S. Department of Transportation, Federal Highway Administration there are more than 4.1 million miles of road in total length, making it the world’s longest and biggest road network with millions of culverts hidden underneath the road in the United States. The development of underground infrastructure, concerns about environmental impacts, and economic trends are influencing society, resulting in the advancement of technology for more environment-friendly and cost-effective pipeline rehabilitation. Trenchless technologies employ innovative methods, materials, and equipment that require minimum surface excavation and access area for rehabilitation of old and deteriorated culverts. Trenchless technologies can be used when other conventional methods, such as open-cut methods, are not applicable or cost-effective. Life-cycle cost analysis (LCCA) is an analytical method used to evaluate long-term investment options, and facilitate the associated costs which consist of three main modules as construction, environmental, and social costs. The main objective of this research is to develop a machine learning-based prediction model for the comprehensive construction and environmental costs of trenchless spray-applied pipe linings (SAPLs), cured-in-place pipe (CIPP), and sliplining methods by evaluation and analysis of the construction and environmental costs based on the actual data. The secondary objective of this research is to compare and analyze the results of construction and environmental costs for SAPL, CIPP, and sliplining in large diameter culverts. Developing a model of construction and environmental costs of a pipeline renewal and is an essential element when considering sustainable development of underground infrastructure. Project owners, decision-makers, design and consulting, and contractors commonly take into consideration the construction costs only, and overlook the environmental aspects while making a choice between trenchless methods. An actual bid data from 7 Departments of Transportation in the U.S. were used for this research to evaluate and development of the prediction model of the construction and environmental costs for the implication of large diameter trenchless SAPL, CIPP, and sliplining renewal methods. It is found that host pipe length, diameter, location, rehab material, and rehab thickness were the key factors to construction cost. Also, material components, the volume of materials, material transportation, project duration and location, and installation equipment were the main influencing factors to environmental cost. The results of this dissertation show that comparing environmental costs of SAPL, CIPP and slipining, SAPL has the lowest and CIPP has the highest costs. The 60-in. diameter is the threshold for changing environmental cost difference between SAPL and the CIPP methods. Above 60-in. diameter, the environmental cost difference between CIPP and SAPL will increase by more than 50%. For diameters 78 in. to 108 in., the environmental costs of CIPP and sliplining are slightly the same and become twice SAPL application. In addition, the difference between mean construction costs of sliplining and SAPL in 72 in. diameter is 120 times more than that of 30 in. diameter. It shows a significant difference exists for construction costs of SAPL and sliplining by increasing the diameter of the culverts. It can be concluded that many quantifiable factors impact SAPL, CIPP, and sliplining construction and environmental costs for large diameter culverts. The prediction model developed in this dissertation provides a tool to compare and evaluate environmental and construction costs of SAPL, CIPP and sliplining for large diameter culverts and storm sewers.


Trenchless technology, Pipe renewal, CIPP, SAPL, Sliplining, Machine learning, Cost prediction model, Environmental cost, Construction cost


Civil and Environmental Engineering | Civil Engineering | Engineering


Degree granted by The University of Texas at Arlington