Automatic Generation of Precast Concrete Component Fabrication Drawings Based on BIM and Multi-Agent Reinforcement Learning
Fabrication drawings are essential for design evaluation, lean manufacturing, and quality detection of precast concrete (PC) components. Due to the complicated shape of PC components, the fabrication drawing needs to be customized to determine manufacturing dimensions and relevant assembly connectio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/2/284 |
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| author | Chao Zhang Xuhong Zhou Chengran Xu Zhou Wu Jiepeng Liu Hongtuo Qi |
| author_facet | Chao Zhang Xuhong Zhou Chengran Xu Zhou Wu Jiepeng Liu Hongtuo Qi |
| author_sort | Chao Zhang |
| collection | DOAJ |
| description | Fabrication drawings are essential for design evaluation, lean manufacturing, and quality detection of precast concrete (PC) components. Due to the complicated shape of PC components, the fabrication drawing needs to be customized to determine manufacturing dimensions and relevant assembly connections. However, the traditional manual drawing method is time-consuming, labor-intensive, and error-prone. This paper presents a BIM-based framework to automatically generate the readable drawing of PC components using building information modeling (BIM) and multi-agent reinforcement learning (MARL). Firstly, an automated generation method is developed to transform BIM model to view block. Secondly, a graph-based representation method is used to create the relationship between blocks, and a reward mechanism is established according to the drawing readability criterion. Subsequently, the block layout is modeled as a layout optimization problem, and the internal spacing and position of functional category blocks are regarded as agents. Finally, the agents collaborate and interact with the environment to find the optimal layout with the guidance of a reward mechanism. Two different algorithms are utilized to validate the efficiency of the proposed method (MADQN). The proposed framework is applied to PC stairs and a double-sided shear wall to demonstrate its practicability. |
| format | Article |
| id | doaj-art-d24d24bb8dc549a0a43bfdfbf6480f67 |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-d24d24bb8dc549a0a43bfdfbf6480f672025-01-24T13:26:28ZengMDPI AGBuildings2075-53092025-01-0115228410.3390/buildings15020284Automatic Generation of Precast Concrete Component Fabrication Drawings Based on BIM and Multi-Agent Reinforcement LearningChao Zhang0Xuhong Zhou1Chengran Xu2Zhou Wu3Jiepeng Liu4Hongtuo Qi5School of Civil Engineering, Chongqing University, Chongqing 400044, ChinaSchool of Civil Engineering, Chongqing University, Chongqing 400044, ChinaSchool of Civil Engineering, Chongqing University, Chongqing 400044, ChinaSchool of Automation, Chongqing University, Chongqing 400044, ChinaSchool of Civil Engineering, Chongqing University, Chongqing 400044, ChinaSchool of Civil Engineering, Chongqing University, Chongqing 400044, ChinaFabrication drawings are essential for design evaluation, lean manufacturing, and quality detection of precast concrete (PC) components. Due to the complicated shape of PC components, the fabrication drawing needs to be customized to determine manufacturing dimensions and relevant assembly connections. However, the traditional manual drawing method is time-consuming, labor-intensive, and error-prone. This paper presents a BIM-based framework to automatically generate the readable drawing of PC components using building information modeling (BIM) and multi-agent reinforcement learning (MARL). Firstly, an automated generation method is developed to transform BIM model to view block. Secondly, a graph-based representation method is used to create the relationship between blocks, and a reward mechanism is established according to the drawing readability criterion. Subsequently, the block layout is modeled as a layout optimization problem, and the internal spacing and position of functional category blocks are regarded as agents. Finally, the agents collaborate and interact with the environment to find the optimal layout with the guidance of a reward mechanism. Two different algorithms are utilized to validate the efficiency of the proposed method (MADQN). The proposed framework is applied to PC stairs and a double-sided shear wall to demonstrate its practicability.https://www.mdpi.com/2075-5309/15/2/284precast concrete (PC) componentfabrication drawinglayout optimization problemmulti-agent reinforcement learning (MARL)building information modeling (BIM) |
| spellingShingle | Chao Zhang Xuhong Zhou Chengran Xu Zhou Wu Jiepeng Liu Hongtuo Qi Automatic Generation of Precast Concrete Component Fabrication Drawings Based on BIM and Multi-Agent Reinforcement Learning Buildings precast concrete (PC) component fabrication drawing layout optimization problem multi-agent reinforcement learning (MARL) building information modeling (BIM) |
| title | Automatic Generation of Precast Concrete Component Fabrication Drawings Based on BIM and Multi-Agent Reinforcement Learning |
| title_full | Automatic Generation of Precast Concrete Component Fabrication Drawings Based on BIM and Multi-Agent Reinforcement Learning |
| title_fullStr | Automatic Generation of Precast Concrete Component Fabrication Drawings Based on BIM and Multi-Agent Reinforcement Learning |
| title_full_unstemmed | Automatic Generation of Precast Concrete Component Fabrication Drawings Based on BIM and Multi-Agent Reinforcement Learning |
| title_short | Automatic Generation of Precast Concrete Component Fabrication Drawings Based on BIM and Multi-Agent Reinforcement Learning |
| title_sort | automatic generation of precast concrete component fabrication drawings based on bim and multi agent reinforcement learning |
| topic | precast concrete (PC) component fabrication drawing layout optimization problem multi-agent reinforcement learning (MARL) building information modeling (BIM) |
| url | https://www.mdpi.com/2075-5309/15/2/284 |
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