开发基于BIM的综合智能模型,用于工程组装过程中的多目标优化.
1School of Architecture and Engineering, Guangdong Polytechnic of Science and Technology, Zhuhai, China.
PloS one
|November 19, 2025
概括
本研究介绍了一种使用建筑信息建模 (BIM) 语义表示的智能优化模型,以提高建筑效率. 该模型平衡了预制建筑的项目持续时间,成本和资源冲突.
科学领域:
- 建设管理建设管理.
- 建筑中的人工智能
- 建筑信息建模 (BIM) 是指建筑信息建模.
背景情况:
- 预制建筑由于资源有限而面临效率和经济性能的挑战.
- 在施工计划 (持续时间,成本,资源) 中优化多目标权衡是复杂的.
- 现有的调度模型往往缺乏适应性和实时反机制.
研究的目的:
- 为预制建筑项目开发一个集成的智能优化模型.
- 通过优化装配计划来提高施工效率和经济绩效.
- 为了在施工期,预算成本和资源冲突之间达成平衡的妥协.
主要方法:
- 使用建筑信息建模 (BIM) 和BuildingNet数据集构建了一个汇编语义模型.
- 采用了具有动态目标权重的多目标粒子群优化 (MOPSO) 算法.
- 集成了一个基于深度Q网络 (DQN) 的强化学习策略,用于实时反和自适应性政策更新.
主要成果:
- 该模型生成了可行的解决方案,用于100个组装任务,在不同的目标重量下设置可行的解决方案.
- 实现了平均85.2天的施工时间,预算成本为1486万美元,资源冲突最小 (<1.7事件).
- 在客观覆盖范围,融合速度和解决方案多样性 (HV=0.683,Spread=0.227,IGD=0.017) 方面表现优于基于规则的NSGA-II和静态MOPSO模型.
结论:
- 综合模型有效地支持预制项目中的动态多目标建筑优化.
- 将语义建模,进化优化和强化学习相结合,可以提高BIM实践的效益-时间表-资源协调.
- 该方法为建筑管理中复杂的权衡场景提供了强大而适应性的解决方案.
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