Open Access

ARTICLE

An Intelligent Optimization Method of Reinforcing Bar Cutting for Construction Site

Zhaoxi Ma¹, Qin Zhao^1,*, Tianyou Cang¹, Zongjian Li², Yiyun Zhu¹, Xinhong Hei³
1 Department of Civil Engineering and Architecture, Xi’an University of Technology, Xi’an, 710048, China
2 State Key Laboratory of Rail Transit Engineering Informatization (FSDI), Xi’an, 710043, China
3 Department of Computer Science and Engineering, Xi’an University of Technology, Xi’an, 710048, China
* Corresponding Author: Qin Zhao. Email:
(This article belongs to this Special Issue: Numerical Methods in Engineering Analysis, Data Analysis and Artificial Intelligence)

Computer Modeling in Engineering & Sciences 2023, 134(1), 637-655. https://doi.org/10.32604/cmes.2022.021216

Received 02 January 2022; Accepted 06 February 2022; Issue published 24 August 2022

Abstract

To meet the requirements of specifications, intelligent optimization of steel bar blanking can improve resource utilization and promote the intelligent development of sustainable construction. As one of the most important building materials in construction engineering, reinforcing bars (rebar) account for more than 30% of the cost in civil engineering. A significant amount of cutting waste is generated during the construction phase. Excessive cutting waste increases construction costs and generates a considerable amount of COCO₂ emission. This study aimed to develop an optimization algorithm for steel bar blanking that can be used in the intelligent optimization of steel bar engineering to realize sustainable construction. In the proposed algorithm, the integer linear programming algorithm was applied to solve the problem. It was combined with the statistical method, a greedy strategy was introduced, and a method for determining the dynamic critical threshold was developed to ensure the accuracy of large-scale data calculation. The proposed algorithm was verified through a case study; the results confirmed that the rebar loss rate of the proposed method was reduced by 9.124% compared with that of traditional distributed processing of steel bars, reducing CO₂ emissions and saving construction costs. As the scale of a project increases, the calculation quality of the optimization algorithm for steel bar blanking proposed also increases, while maintaining high calculation efficiency. When the results of this study are applied in practice, they can be used as a sustainable foundation for building informatization and intelligent development.

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Keywords

Building construction; rebar work; cutting stock problem; optimization algorithm; integer linear programming

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