Date of Award

Fall 2013

Document Type


Degree Name

Doctor of Philosophy (PhD)


Civil Engineering

First Advisor

Erez N. Allouche


The research work presented in this thesis has two broad objectives as well as five individual goals. The first objective is to search and determine the minimum cost and corresponding goodness-of-fit by using a different combination of methods that are capable of resolving the problem that exists in multiple segments. This approach can account for variations in unit price and the cost of the design and the inspection associated with multiple methods. The second objective is to calculate the minimum risk for the preferred solution set. The five individual goals are 1) reduction in total cost, 2) application of Genetic Algorithm (GA) for construction method selection with focus on trenchless technology, 3) application of Fuzzy Inference System for likelihood of risk, 4) risk assessment in HDD projects, and 5) Carbon footprint calculation.

In most construction projects, multiple segments are involved in a single project. However, there is no single model developed yet to aid the selection of appropriate method(s) based on the consideration of multiple-criteria. In this study, a multi-segment conceptualizes a combination of individuals or groups of mainlines, manholes, and laterals. Multi-criteria takes into account the technical viability, direct cost, social cost, carbon footprint, and risks in the pipelines. Three different segments analyzed are 1) an 8 inch diameter, 280 foot long gravity sewer pipe, 2) a 21 inch diameter, 248 foot long gravity sewer pipe, and 3) a 12 inch diameter, 264 foot long gravity sewer pipe. It is found that GA would not only eliminate the shortcomings of competing mathematical approaches, but also enables complex optimization scenarios to be examined quickly to the optimization of multi-criteria for multi-segments.

Furthermore, GA follows a uniform iterative procedure that is easy to code and decode for running the algorithm.

Any trenchless installation project is associated with some level of risk. Due to the underground installation of trenchless technologies, the buried risk could be catastrophic if not assessed promptly. Therefore, risk management plays a key role in the construction of utilities. Conventional risk assessment approach quantifies risk as a product of likelihood and severity of risk, and does not consider the interrelation among different risk input variables. However, in real life installation projects, the input factors are interconnected, somewhat overlapped, and exist with fuzziness or vagueness.

Fuzzy logic system surpasses this shortcoming and delivers the output through a process of fuzzification, fuzzy inference, fuzzy rules, and defuzzification. It is found in the study that Mamdani FIS has the potential to address the fuzziness, interconnection, and overlapping of different input variables and compute an overall risk output for a given scenario which is beyond the scope of conventional risk assessment.