Minimization of Rebar Cutting Waste Using BIM and Cutting Pattern-Oriented Multiobjective Optimization
Publication: Journal of Construction Engineering and Management
Volume 150, Issue 11
Abstract
Rebars are among the main materials used in reinforced concrete structures and among the costliest and most carbon-intensive construction materials. However, during the construction phase, rebar cutting schemes are usually not rationalized enough and may lead to up to 8% waste. Previous studies and practices showed that obtaining data on rebar cutting can be labor intensive and imprecise, lacking the profiling of quantity take-off factors. Additionally, existing optimization techniques often do not adequately consider field characteristics, such as the number of cutting patterns and the scale of the problem, and the characteristics of the cutting data. This study proposes a practical, integrated approach for minimizing rebar cutting waste during the construction phase. The proposed approach consists of two main parts. The first part is obtaining rebar cutting data based on building information modeling (BIM). In this paper, the main influencing factors of rebar quantity take-off are analyzed, and the modeling hierarchy is divided based on these factors to obtain accurate cutting data. The hierarchy also reflects repeatable commonalities between different rebar systems, which helps reduce repetitive modeling efforts and increase efficiency. In the second part, a cutting pattern-oriented multiobjective optimization method is proposed to optimize the cutting scheme. The process is divided into three stages: generating valid cutting patterns, optimizing their frequency, and combining a small number of remaining components. The method combines three algorithms—column generation, particle swarm optimization, and best-fit decreasing—to solve the problem according to the characteristics of each stage. The influence of multiobjectives on the final optimization result is further explored through a sensitivity analysis. The integrated approach was validated with test sets of different sizes, types, and complexities. Its effectiveness and generalizability in reducing rebar cutting waste contribute to its applicability and the achievement of construction sustainability goals.
Practical Applications
Rebars are among the main materials used in reinforced concrete structures and among the costliest and most carbon-intensive construction materials. Due to the extensiveness of the construction management mode, rebar has inevitably become one of the main sources of construction waste. However, due to the lack of practical optimization methods, this percentage can reach 5%–8% or even higher. This study proposes a practical approach for minimizing rebar cutting waste during the construction phase. In this paper, the main influencing factors of rebar quantity take-off are analyzed, and the modeling hierarchy is divided based on these factors to obtain accurate cutting data. Then, the cutting patterns are optimized, including generating valid cutting patterns, optimizing the frequency of the cutting patterns, combining a small number of remaining components, and organizing them into cutting schemes. The integrated approach provides a flexible perspective for solving this problem. The approach was tested for its generalizability and adaptability in various construction scenarios using test sets of different sizes, types, and complexities. The effectiveness of the approach in reducing rebar cutting waste, combined with the consideration of field characteristics and engineering habits, contributes to its applicability and the achievement of construction sustainability goals.
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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 work was supported by the Science and Technology Plan Project of Jiangxi Geological Bureau (Grant No. 2023JXDZKJKY07); the Science and Technology Plan Project of Jiangxi Geological Bureau (Grant No. 2021JXDZ70001); and the Science and Technology Plan Project of Jiangxi Coalfield Geology Bureau (Grant No. 2020JXMD70003).
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Journal of Construction Engineering and Management
Volume 150 • Issue 11 • November 2024
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© 2024 American Society of Civil Engineers.
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Received: Jan 27, 2024
Accepted: Jun 7, 2024
Published online: Sep 10, 2024
Published in print: Nov 1, 2024
Discussion open until: Feb 10, 2025
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