Sustainability in Prefabricated Construction: Enhancing Multicriteria Analysis and Prediction Using Machine Learning
Publication: Journal of Construction Engineering and Management
Volume 150, Issue 8
Abstract
Multicriteria analysis is widely used to prove the excellence of prefabricated construction compared with conventional construction. However, because previous studies have not presented the results of an integrated analysis, identifying the merits of prefabricated construction is challenging. Furthermore, clients experience difficulty when considering prefabricated construction owing to the complexity of simulations and the lack of data. Therefore, this study aimed to conduct a multicriteria analysis for prefabricated construction considering productivity, safety, environment, and economy, and develop a multi-prediction model. This study was conducted in five stages. Results revealed that prefabricated construction was superior to conventional construction for all variables, with the former scoring 0.0927 on average and the latter scoring 1.863. The multiprediction model utilizing a decision tree and Bayesian optimization has a high performance, achieving over 94%. Using study findings, decision makers can use the multiprediction model to assess the expected performance of prefabricated construction. This enables a comprehensive comparison of various conditions across different aspects through the multicriteria analysis.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was financially supported by the Ministry of Trade, Industry, and Energy (MOTIE) and the Korea Institute for Advancement of Technology (KIAT) through the International Cooperative R&D Program (Project No. P0017191).
Author contributions: Jaemin Jeong: conceptualization, resources, methodology, formal analysis, validation, visualization, and writing–original draft. Jaewook Jeong: supervision, resources, project administration, and writing–review and editing.
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Published In
Journal of Construction Engineering and Management
Volume 150 • Issue 8 • August 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jul 7, 2023
Accepted: Feb 27, 2024
Published online: May 24, 2024
Published in print: Aug 1, 2024
Discussion open until: Oct 24, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Business management
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction equipment
- Construction industry
- Construction management
- Engineering fundamentals
- Equipment and machinery
- Models (by type)
- Offsite construction
- Optimization models
- Personnel management
- Practice and Profession
- Productivity
- Sustainable development
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