Soft Computing-based Life-Cycle Cost Analysis Tools for Transportation Infrastructure Management

Increasing demands, shrinking financial and human resources, and increased infrastructure deterioration have made the task of maintaining the infrastructure systems more challenging than ever before. Life-cycle cost analysis (LCCA) is an important tool for transportation infrastructure management, which is used extensively to support project level decisions, and is increasingly being applied to enhance network level analysis. However, traditional LCCA tools cannot practically and effectively utilize expert knowledge and handle ambiguous uncertainties. The main objective of this dissertation was to develop enhanced LCCA models using soft computing (mainly fuzzy logic) techniques. The proposed models use available “realworld” information to forecast life-cycle costs of competing maintenance and rehabilitation strategies and support infrastructure management decisions. A critical review of available soft computing techniques and their applications in infrastructure management suggested that these techniques provide appealing alternatives for supporting many of the infrastructure management functions. In particular, LCCA often utilizes information that is uncertain, ambiguous and incomplete, which is obtained from both existing databases and expert opinion. Consequently, fuzzy logic techniques were selected to enhance life-cycle cost analysis of transportation infrastructure investments because they provide a formal approach for the effective treatment of these types of information. The dissertation first proposes a fuzzy-logic-based decision-support model, whose inference rules can be customized according to agency’s management policies and expert opinion. The feasibility and practicality of the proposed model is illustrated by its