Empirical Inference System for Highway Project Delivery Selection Using Fuzzy Pattern Recognition
Publication: Journal of Construction Engineering and Management
Volume 146, Issue 12
Abstract
Selection of a project delivery method in highway construction is a challenging task because of the many decision criteria involved. In addition to quantitative project attributes, the project delivery decision-making process also typically relies on qualitative measures such as subjective judgments and experts’ opinions based on their experience with similar completed projects. Although current probabilistic methods provide a robust means to analyze quantitative data, they are not ideally suited for treating uncertainties encountered in qualitative data. To overcome the identified gap, this study investigated fuzzy pattern recognition, a mathematical technique based on fuzzy sets and fuzzy logic, to model a combination of quantitative and qualitative variables in highway project delivery selection. A fuzzy rule-based inference system was developed, trained, and tested using 254 empirical highway projects with particular project attributes including project type, project complexity, delivery risk, and cost performance. The proposed system was verified by performing a case project with the result of accurately recognizing the true project delivery method used and associated cost growth performance expectations. The flexibility of fuzzy membership functions in the proposed system helps leverage the evaluation of a combination of quantitative and qualitative variables in project delivery method selection in complex highway construction projects. In addition, this data-driven fuzzy inference system also allows for multiple decision scenarios based on the decision maker’s judgments of delivery risks and project complexity. This study contributes to the body of knowledge by developing an empirical inference system to recognize possible patterns of delivery methods associated with cost growths for new highway projects. This study may assist highway agencies in making project delivery decisions based on project attributes, historical data, and their relevant experience.
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Data Availability Statement
Data generated or analyzed during the study are available from the corresponding author by request. Information about the Journal’s data-sharing policy can be found here: http://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
Acknowledgments
The authors would like to gratefully acknowledge the Federal Highway Administration (FHWA) and Dr. Keith Molenaar from the University of Colorado Boulder for helping with data collection used in this paper.
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Published In
Journal of Construction Engineering and Management
Volume 146 • Issue 12 • December 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Nov 1, 2019
Accepted: Jul 20, 2020
Published online: Sep 30, 2020
Published in print: Dec 1, 2020
Discussion open until: Feb 28, 2021
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