Fuzzy Set Theory Approach to Classify Highway Project Characteristics for Delivery Selection

Abstract

Selecting an appropriate delivery method for highway projects is a complex decision process. Delivery decisions typically involve evaluating both quantitative (e.g., cost, schedule) and qualitative (e.g., project complexity, delivery risk) criteria. Although many studies have proposed processes and guidance to select the most suitable delivery method, there is a lack of understanding on how to rigorously address qualitative criteria. This study aimed at applying fuzzy cluster analysis to investigate cost performance associated with qualitative criteria, including project complexity and delivery risk. The proposed approach includes three main steps: (1) assess data clustering tendency; (2) determine number of clusters; and (3) validate clustering result. An empirical data set of 254 completed highway projects was used to develop and illustrate the proposed approach. The result shows seven clusters for comparing cost performance between design-bid-build (D-B-B) and design-build (D-B) projects. D-B-B produces low-cost growth for Project Cluster 1, but medium- to high-cost growth for Project Cluster 3. D-B produces low-cost growth for Project Clusters 2, 4, 5, and 7. For Cluster 6, there is no difference in cost growth between D-B-B and D-B. The findings also indicate that D-B outperformed D-B-B in new, complex, and highly risky projects, whereas D-B-B was a better choice in certain reconstruction projects. This study contributes to the body of knowledge by identifying seven groups of highway projects that have many commonalities with respect to project attributes and cost performance associated with the use of D-B-B and D-B. The identified groups may help highway agencies better understand and select the most suitable delivery method for their projects.