Optimizing Modularization of Residential Housing Designs for Rapid Postdisaster Mass Production of Housing
Publication: Journal of Construction Engineering and Management
Volume 149, Issue 7
Abstract
Postdisaster housing reconstruction (PDHR) is a highly complex process because of the large number of recovery projects for affected communities and the shortage of resources after a disastrous event. PDHR also needs a strategy that reconsiders it as a large-scale integrated portfolio of projects instead of individual building reconstruction projects. However, this complexity and the lack of a holistic, systematic approach for planning frequently lead to an ad-hoc or case-by-case decision-making process. To resolve this critical challenge in postdisaster housing mass production, this study investigates and develops a systematic approach that optimizes the design modularization of housing recovery projects considering manufacturing, transportation, and assembly factors for a cost-efficient and sustainable implication of modular construction (MC) in PDHR. Using the genetic algorithm-based optimization method, the proposed method addresses the possible trade-offs between the commonality and suitability of the module configurations for PDHR projects. In addition, the authors used a set of feasible configurations of a variety of modular housing designs created from the AI-based generative design system and conducted the mass production scenarios after a disaster to validate the accuracy and robustness of the proposed methodology. The results clearly show that the proposed method significantly improved optimization and decision-making of MC design and construction processes and considerably enhanced rapid and logical responses to the demands of the postdisaster recovery process. The newly developed method is expected to assist the planners in formalizing the commonality concept in the PDHR process and achieving an optimal level of modularity and commonality that meet the required variation while maintaining the advantages of mass production.
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Data Availability Statement
All data, models, or code generated or used during the study are available from the corresponding author by request.
Acknowledgments
The authors acknowledge the research support of the Louisiana Economic Development Assistantship (EDA).
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Journal of Construction Engineering and Management
Volume 149 • Issue 7 • July 2023
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© 2023 American Society of Civil Engineers.
History
Received: Oct 31, 2021
Accepted: Jun 16, 2022
Published online: Apr 26, 2023
Published in print: Jul 1, 2023
Discussion open until: Sep 26, 2023
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