Ke ZhangThis email address is being protected from spambots. You need JavaScript enabled to view it.
Henan College of Industry & Information Technology, Jiaozuo 454000, China
Received: May 23, 2024 Accepted: July 12, 2025 Publication Date: August 1, 2025
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
In traditional construction projects, the arrangement of bricks is time-consuming and inaccurate, especially when dealing with irregular Spaces. In order to improve the construction efficiency and reduce the number of bricks cut, an optimization method based on backtracking strategy (BS) and improved neighborhood search algorithm (NS) is proposed, combined with artificial group algorithm, chaos theory and reverse learning to improve the convergence speed. The improved backtracking and neighborhood Search Optimization (IBT) model was trained and validated based on 5,800 brick layout data provided by a single construction company. The results showed that the fitness of IBT model reached 99.98% on the training set, which was better than SPEA-2-GA (96.45%) and EOM (88.74%). The accuracy on the validation set was 98.89%. The resource consumption of the IBT model was significantly reduced, with only 58 percent of the processor used on the 7th count, compared to 97.5 percent for the other models. Based on large-scale multi-enterprise data, compared with existing algorithms, the IBT model significantly reduces the number of cuts by optimizing the block arrangement, with an optimization rate of 18.6%. This model can effectively improve the construction efficiency and reduce the consumption of system resources. It is suitable for rectangular bricks and single data source scenarios, especially for construction units with limited resources.
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