Automatic Generation of Precast Concrete Component Fabrication Drawings Based on BIM and Multi-Agent Reinforcement Learning
Author(s): |
Chao Zhang
Xuhong Zhou Chengran Xu Zhou Wu Jiepeng Liu Hongtuo Qi |
---|---|
Medium: | journal article |
Language(s): | English |
Published in: | Buildings, 15 January 2025, n. 2, v. 15 |
Page(s): | 284 |
DOI: | 10.3390/buildings15020284 |
Abstract: |
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. |
Copyright: | © 2025 by the authors; licensee MDPI, Basel, Switzerland. |
License: | This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met. |
16.3 MB
- About this
data sheet - Reference-ID
10816189 - Published on:
03/02/2025 - Last updated on:
03/02/2025