A Data-Driven Decision-Support Tool for Selecting the Optimal Project Delivery Method for Bundled Projects: Integrating Machine Learning and Expert Domain Knowledge
Publication: Journal of Construction Engineering and Management
Volume 150, Issue 12
Abstract
Project bundling is an innovative project delivery approach that combines several projects under a single contract. While previous studies have provided important information about different project bundling-related aspects, none have developed guidelines to choosing the best project delivery method (PDM) for bundled projects/contracts. Also, despite that some of the existing research efforts have offered tools to identify the optimal PDM, such studies were conducted for single projects rather than for bundled projects which significantly differ from a normal project in terms of complexity and implementation considerations. Hence, this paper develops a data-driven decision support tool that helps agencies in identifying the optimal PDM for their bundled projects by leveraging machine learning algorithms and domain knowledge while also considering the characteristics and goals of the bundled program. This proposed tool considers and compares the following 5 PDMs: design bid build (DBB); design build (DB); construction manager/general contractor (CM/GC); indefinite delivery/indefinite quantity (IDIQ); and public private partnership (PPP). First, data from previous project bundling case studies were used to identify bundling opportunities (on the program or strategic level) as well as bundling objectives (on the project or contract level). Second, a machine learning model (i.e., multinomial naïve Bayes classifier) was developed to generate a probabilistic distribution for the relative suitability of the five PDMs on the strategic bundling program level. Third, a survey was developed and distributed to collect expert’s domain knowledge on the importance of the different project bundling objectives (i.e., on the project or contract level). Lastly, an easy-to-use decision-support tool was developed to calculate individual scores for the different 5 PDMs so that the best PDM could be identified. Ultimately, this paper presents an intuitive and easy to implement tool for selecting PDMs for bundled projects based on the integration of machine learning algorithms and domain knowledge.
Practical Applications
This paper provides numerous practical applications. More specifically, this research equips project owners with a data-driven tool to select the optimal PDM for their bundled projects by considering factors and characteristics on the overall program level as well as on the project level. This tool also helps in properly allocating risks between the project parties or stakeholders involved in the delivery of bundled projects. The developed tool in this paper is integrated into user-friendly excel spreadsheets with automated calculations, and thus it automates the PDM-related decision-making process, which makes it easy to implement in practice. Consequently, by selecting the optimal PDM, the proposed decision-support tool would enable project owners to enhance their bundling practices, improve the performance of their bundled program, maintain costs within identified budgets, improve the schedule of their bundled projects, and better capitalize on the various benefits of the bundling approach.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This manuscript is based upon work supported by the US Department of Transportation, Office of the Assistant Secretary for Research and Technology (OST-R) under Grant No. 69A3551847102 through the Center for Advanced Infrastructure and Transportation (CAIT)Region 2 UTC Consortium Led by Rutgers, The State University of New Jersey (Project No. CAIT-UTC-REG68). Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the funding agency.
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Journal of Construction Engineering and Management
Volume 150 • Issue 12 • December 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jan 29, 2024
Accepted: Jul 9, 2024
Published online: Oct 12, 2024
Published in print: Dec 1, 2024
Discussion open until: Mar 12, 2025
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