Fuzzy Set Theory Approach to Classify Highway Project Characteristics for Delivery Selection
Publication: Journal of Construction Engineering and Management
Volume 146, Issue 5
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.
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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/%28ASCE%29CO.1943-7862.0001263.
Acknowledgments
The authors gratefully acknowledge the Federal Highway Administration (FHWA) and Dr. Keith Molenaar from the University of Colorado Boulder for helping with the data collection used in this paper.
References
Al Khalil, M. I. 2002. “Selecting the appropriate project delivery method using AHP.” Int. J. Project Manage. 20 (6): 469–474. https://doi.org/10.1016/S0263-7863(01)00032-1.
Al Nahyan, M., Y. Hawas, M. Raza, H. Aljassmi, M. Maraqa, B. Basheerudeen, and M. Mohammad. 2018. “A fuzzy-based decision support system for ranking the delivery methods of mega projects.” Int. J. Managing Projects Business 11 (1): 122–143. https://doi.org/10.1108/IJMPB-06-2017-0055.
Ammar, M., T. Zayed, and O. Moselhi. 2013. “Fuzzy-based life-cycle cost model for decision making under subjectivity.” J. Constr. Eng. Manage. 139 (5): 556–563. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000576.
Anderberg, M. R. 2014. Cluster analysis for applications: Probability and mathematical statistics: A series of monographs and textbooks. New York: Academic Press.
Bakht, M., and T. El-Diraby. 2015. “Synthesis of decision-making research in construction.” J. Constr. Eng. Manage. 141 (9): 04015027. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000984.
Bypaneni, S. 2017. “A BN-based decision framework for selecting project delivery methods in highway construction.” Ph.D. dissertation, Dept. of Civil, Environmental, and Architectural Engineering, Univ. of Kansas.
Chan, A., D. Chan, and J. Yeung. 2009. “Overview of the application of “fuzzy techniques” in construction management research.” J. Constr. Eng. Manage. 135 (11): 1241–1252. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000099.
Chen, Q., Z. Jin, B. Xia, P. Wu, and M. Skitmore. 2016. “Time and cost performance of design–build projects.” J. Constr. Eng. Manage. 142 (2): 04015074. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001056.
CII (Construction Industry Institute). 2018. Revisiting project delivery performance. McLean, VA: Charles Pankow Foundation.
Col Debella, D. M., and R. Ries. 2006. “Construction delivery systems: A comparative analysis of their performance within school districts.” J. Constr. Eng. Manage. 132 (11): 1131–1138. https://doi.org/10.1061/(ASCE)0733-9364(2006)132:11(1131).
Diab, M., A. Varma, and K. Nassar. 2012. “Using risk assessment to improve highway construction project performance.” In Proc., 48th ASC Annual Int. Conf. Fort Collins, CO: Associated Schools of Construction.
Douglas, A., A. Antoine, M. Schrilla, and K. Molenaar. 2016. “The use and performance of alternative contracting methods on small highway construction projects.” Procedia Eng. 145: 908–915. https://doi.org/10.1016/j.proeng.2016.04.118.
D’Urso, P. 2007. “Fuzzy clustering of fuzzy data.” In Advances in fuzzy clustering and its applications, edited by J. V. de Oliveira, and W. Pedrycz, 155–192. West Sussex, UK: Wiley.
Elbarkouky, M., A. Fayek, N. Siraj, and N. Sadeghi. 2016. “Fuzzy arithmetic risk analysis approach to determine construction project contingency.” J. Constr. Eng. Manage. 142 (12): 04016070. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001191.
Elwood, E., and R. B. Corotis. 2015a. “A clustering approach to identification of seismic building damage patterns for concrete structures.” In Proc., 12th Int. Conf. on Applications of Statistics and Probability in Civil Engineering, ICASP12. Vancouver, BC, Canada: Univ. of British Columbia.
Elwood, E., and R. B. Corotis. 2015b. “Application of fuzzy pattern recognition of seismic damage to concrete structures.” ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. 1 (4): 04015011. https://doi.org/10.1061/AJRUA6.0000831.
FHWA (Federal Highway Administration). 2018. TECHBRIEF: Alternative contracting method performance in U.S. highway construction. Washington, DC: FHWA.
Goodrum, P., M. Uddin, and B. Faulkenberg. 2011. A case study analysis of the Kentucky Transportation Cabinet’s design/build pilot projects. Lexington, KY: Kentucky Transportation Center Research.
Gransberg, D. D., and J. S. Shane. 2010. Construction manager-at-risk project delivery for highway programs. Washington, DC: Transportation Research Board.
Hale, D. R., P. P. Shrestha, G. E. Gibson Jr., and G. C. Migliaccio. 2009. “Empirical comparison of design/build and design/bid/build project delivery methods.” J. Constr. Eng. Manage. 135 (7): 579–587. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000017.
Hoppner, F., F. Klawonn, R. Kruse, and T. Runkler. 1999. Fuzzy cluster analysis methods for classification, data analysis and image recognition. New York: Wiley.
Ibbs, C. W., Y. H. Kwak, T. Ng, and A. M. Odabasi. 2003. “Project delivery systems and project change: Quantitative analysis.” J. Constr. Eng. Manage. 129 (4): 382–387. https://doi.org/10.1061/(ASCE)0733-9364(2003)129:4(382).
Kassambara, A. 2017. “Practical guide to cluster analysis in R: Unsupervised machine learning (Multivariate Analysis).” Accessed December 15, 2018. https://www.datanovia.com/en/wp-content/uploads/dn-tutorials/book-preview/clustering_en_preview.pdf.
Konchar, M., and V. Sanvido. 1998. “Comparison of US project delivery systems.” J. Constr. Eng. Manage. 124 (6): 435–444. https://doi.org/10.1061/(ASCE)0733-9364(1998)124:6(435).
Kruse, R., C. Doring, and M.-J. Lesot. 2007. “Fundamentals of fuzzy clustering.” In Advances in fuzzy clustering and its applications, edited by J. V. de Oliveira and W. Pedrycz, 3–30. West Sussex, UK: Wiley.
Lam, K., A. So, T. Hu, T. Ng, R. Yuen, S. Lo, S. O. Cheung, and H. Yang. 2001. “An integration of the fuzzy reasoning technique and the fuzzy optimization method in construction project management decision-making.” Constr. Manage. Economics 19 (1): 63–76. https://doi.org/10.1080/014461901452085.
Li, J., O. Moselhi, and S. Alkass. 2006. “Forecasting project status by using fuzzy logic.” J. Constr. Eng. Manage. 132 (11): 1193–1202. https://doi.org/10.1061/(ASCE)0733-9364(2006)132:11(1193).
Love, P., X. Wang, C. Sing, and R. Tiong. 2013. “Determining the probability of project cost overruns.” J. Constr. Eng. Manage. 139 (3): 321–330. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000575.
Mahdi, I., and K. Alreshaid. 2005. “Decision support system for selecting the proper delivery method using analytical hierarchy process (AHP).” Int. J. Project Manage. 23 (7): 564–572. https://doi.org/10.1016/j.ijproman.2005.05.007.
Minchin, R., X. Li, R. Issa, and G. Vargas. 2013. “Comparison of cost and time performance of design-build and design-bid-build delivery systems in Florida.” J. Constr. Eng. Manage. 139 (10): 04013007. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000746.
Molenaar, K., and A. Songer. 1998. “Model for public sector design-build project selection.” J. Constr. Eng. Manage. 124 (6): 467–479. https://doi.org/10.1061/(ASCE)0733-9364(1998)124:6(467).
Mostafavi, A., and M. Karamouz. 2010. “Selecting appropriate project delivery system: Fuzzy approach with risk analysis.” J. Constr. Eng. Manage. 136 (8): 923–930. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000190.
Nikou Goftar, V., M. El Asmar, and E. Bingham. 2014. “A meta-analysis of literature comparing project performance between design-build (DB) and design-bid-build (DBB) delivery systems.” In Proc., Construction Research Congress. Atlanta, Georgia: CRC Press.
Pal, N. R., and J. C. Bezdek. 1995. “On cluster validity for the fuzzy c-means model.” IEEE Trans. Fuzzy Syst. 3 (3): 370–379. https://doi.org/10.1109/91.413225.
Park, J., and Y. H. Kwak. 2017. “Design-bid-build (DBB) vs. design-build (DB) in the U.S. public transportation projects: The choice and consequences.” Int. J. Project Manage. 35 (3): 280–295. https://doi.org/10.1016/j.ijproman.2016.10.013.
Pawan, P., and P. Lorterapong. 2016. “A fuzzy-based integrated framework for assessing time contingency in construction projects.” J. Constr. Eng. Manage. 142 (3): 04015083. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001073.
Říhová, E., and T. Makhalova. 2017. “On evaluating of fuzzy clustering results.” In Proc., 11th Int. Days of Statistics and Economics. Prague, Czech Republic: Česká spořitelna.
Rojas, E. M., and I. Kell. 2008. “Comparative analysis of project delivery systems cost performance in Pacific Northwest public schools.” J. Constr. Eng. Manage. 134 (6): 387–397. https://doi.org/10.1061/(ASCE)0733-9364(2008)134:6(387).
Ross, T. J. 2010. Fuzzy logic with engineering applications. 3rd ed. West Sussex, UK: Wiley.
Seo, S., T. Aramaki, Y. Hwang, and K. Hanaki. 2004. “Fuzzy decision-making tool for environmental sustainable buildings.” J. Constr. Eng. Manage. 130 (3): 415–423. https://doi.org/10.1061/(ASCE)0733-9364(2004)130:3(415).
Shrestha, P., G. Migliaccio, J. O’Connor, and G. Gibson. 2007. “Benchmarking of large design–build highway projects.” Transp. Res. Rec. 1994 (1): 17–25. https://doi.org/10.3141/1994-03.
Shrestha, P., J. O’Connor, and G. Gibson. 2012. “Performance comparison of large design-build and design-bid-build highway projects.” J. Constr. Eng. Manage. 138 (1): 1–13. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000390.
Sullivan, J., M. E. Asmar, J. Chalhoub, and H. Obeid. 2017. “Two decades of performance comparisons for design-build, construction manager at risk, and design-bid-build: Quantitative analysis of the state of knowledge on project cost, schedule, and quality.” J. Constr. Eng. Manage. 143 (6): 04017009. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001282.
Touran, A., D. Gransberg, K. Molenaar, and K. Ghavamifar. 2011. “Selection of project delivery method in transit: Drivers and objectives.” J. Manage. Eng. 27 (1): 21–27. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000027.
Touran, A., D. D. Gransberg, K. R. Molenaar, K. Ghavamifar, D. J. Mason, and L. A. Fithian. 2009a. A guidebook for the evaluation of project delivery methods. Washington, DC: Transportation Research Board.
Touran, A., K. Molenaar, D. Gransberg, and K. Ghavamifar. 2009b. “Decision support system for selection of project delivery method in transit.” Transp. Res. Rec. 2111 (1): 148–157. https://doi.org/10.3141/2111-17.
Tran, D., G. Diraviam, and R. Minchin. 2018. “Performance of highway design-bid-build and design-build projects by work types.” J. Constr. Eng. Manage. 144 (2): 04017112. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001437.
Tran, D., and K. Molenaar. 2015. “Risk-based project delivery selection model for highway design and construction.” J. Constr. Eng. Manage. 141 (12): 04015041. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001024.
Tran, D., K. Molenaar, and L. Alarcön. 2016. “A hybrid cross-impact approach to predicting cost variance of project delivery decisions for highways.” J. Infrastruct. Syst. 22 (1): 04015017. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000270.
USDOT, Federal Highway Administration. 2006. “Design-build effectiveness study.” Accessed February 28, 2019. http://www.fhwa.dot.gov/reports/designbuild/designbuild.pdf.
WSDOT (Washington State Department of Transportation). 2016. Project delivery method selection guidance. Washington, UK: WSDOT.
Wu, K., and M. Yang. 2005. “A cluster validity index for fuzzy clustering.” Pattern Recognit. Lett. 26 (9): 1275–1291. https://doi.org/10.1016/j.patrec.2004.11.022.
Wu, X., B. Wu, J. Sun, and H. Fu. 2010. “Unsupervised possibilistic fuzzy clustering.” J. Inf. Comput. Sci. 7 (5): 1075–1080.
Zadeh, L. A. 1965. “Fuzzy sets.” Inf. Control 8 (3): 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X.
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©2020 American Society of Civil Engineers.
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Received: Mar 1, 2019
Accepted: Nov 12, 2019
Published online: Mar 9, 2020
Published in print: May 1, 2020
Discussion open until: Aug 9, 2020
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