The requirements for public utility systems in the United States of America have grown enormously over the years triggering a tremendous shortage for space available to public utilities on and within transportation right-of-ways (ROW). Overcrowding and improper location of utilities has resulted in problems such as, damage to infrastructure, traffic accidents and, interruption of service to customers. The project titled, "Optimal Placement of Utilities within FDOT Right-of-Way", sponsored by the Florida Department of Transportation (FDOT), and currently being investigated at the University of South Florida, presents a decision-making heuristic aimed at developing a better utility placement allocation system (Kranc et. al) [6]. Working in accordance with the guidelines of safety, relocation, and clearance for utility placement set by the American Association of State Highway and Transportation Officials organization (AASHTO), the heuristic finds suitable locations for the utilities in ROW corridors. However, a model being used to advocate a practice having large social and economical impacts is more likely to play the role of generic evidence in a trial, whose weight must ultimately be established by a 'jury'. The question being addressed to the system must be scrutinized carefully, and the formal structure updated iteratively until it proves capable of providing an answer to the given question. A good sensitivity analysis can provide this generic quality assurance to the model and help demonstrate the worthiness of the model itself. This thesis is a quantitative and qualitative sensitivity analysis of the abovementioned heuristic. The analysis is conducted in two parts, 1. The 'Model Factor Sensitivity Analysis', with the objective of assessing the uncertainties associated with the modeling of the heuristic. This analysis focuses primarily on providing an evaluation of the confidence in the heuristic and its predictions by analyzing the influences that variations in the input factors have on the outputs of the utility cost assessment models and the final output of the heuristic itself. Variance based sensitivity indices derived from Sobol' sensitivity indices [42] are used here for this purpose. 2. The 'Model Output Evaluation and Enhancement' study, which initially focuses on understanding / evaluating the complexities of the discrete step, cost optimization procedure used in the heuristic and later, based on certain observed shortcomings and problems develops an enhancement, the Ideal Configuration Selector (ICS) to be implemented with the heuristic. The ICS addresses all the problems of the heuristic with the help of experimental speedup, positional sensitivity and refinement tools and employs a multi criterion evaluation technique for utility configuration assessment to provide substantiation to the outputs determined by the heuristic.

No description available.