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图学学报

图学学报 ›› 2021, Vol. 42 ›› Issue (2): 299-306.DOI: 10.11996/JG.j.2095-302X.2021020299

• 建筑与城市信息模型 • 上一篇    下一篇

基于人工智能的 BIM 疏散设计自动化方法

  

  1. 1. 同济大学建筑设计研究院(集团)有限公司，上海 200092；  2. 上海建筑数字建造工程技术研究中心，上海 200092；  3. 普华永道信息技术(上海)有限公司数字方案部，上海 200021；  4. 同济大学建筑与城市规划学院，上海 200092
  • 出版日期:2021-04-30 发布日期:2021-04-30
  • 基金资助:
    教育部重点实验室(同济大学)开放课题(2019010103)  

BIM evacuation design automation based on artificial intelligence 

  1. 1. Tongji Architectural Design (Group) Co.,Ltd, Shanghai 200092, China;  2. Shanghai Digital Architecture Fabrication Technology Center, Shanghai 200092, China;  3. Department Digital Solutions, PricewaterhouseCoopers Zhong Tian LLP (Shanghai), Shanghai 200021, China;  4. College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China
  • Online:2021-04-30 Published:2021-04-30
  • Supported by:
    Open Projects Fund of Key Laboratory of Ministry of Education (Tongji University) (2019010103) 

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摘要： 针对目前建筑信息模型(BIM)消防疏散路径人工绘制的耗时问题，从提高设计效率出发，提出 了一种基于深度 Q 学习(DQN)与 A*结合的混合算法，并以此开发了一种基于该算法的 BIM 疏散自动设计工具。 首先，房间疏散路径使用 A*算法进行绘制；然后使用改进的 DQN 算法确定楼层疏散中疏散门至安全出口的路 径再以 A*算法绘制。在 DQN 算法的基础上重新设计了奖励矩阵赋值及增加了奖励矩阵验证机制提高了绘制正 确性；最后，使用以该算法为基础的疏散设计自动化工具对实际项目进行了实验。结果表明，该算法不仅能正 确绘制路线并且比手工绘制效率提高 2~3 倍。通过服务器部署及硬件的升级为该算法效率进一步提升提供了可 能。目前基于该算法的自动设计工具已在同济设计院上海建筑数字中心的多项实际项目中使用。

关键词: 人工智能, 建筑信息模型, 疏散, 设计自动化, 深度 Q 学习

Abstract: To reduce the time cost of manual design of the Building Information Modeling (BIM) fire evacuation routes and to enhance design efficiency, an improved algorithm was proposed based on Deep Q Learning (DQN) and A* algorithm, through which a BIM evacuation design automation tool developed. First, the evacuation path of the room was drawn by A* algorithm. Then, the path from the evacuation door to safety exit in floor evacuation was determined using the improved DQN algorithm, and was drawn by A* algorithm. On the basis of the DQN algorithm, we redesigned reward matrix assignment and added reward matrix verification to improve the correctness of floor evacuation. Finally, we applied the improved algorithm-based evacuation design automation tool to practical projects. The results show that this improved algorithm can not only draw the route correctly, but also increase the efficiency by 2 to 3 times compared with manual drawing. The efficiency of this algorithm can be further improved by server deployment and hardware upgrades. At present, the automatic design tool has been adopted in several actual projects of Shanghai Digital Architecture Fabrication Technology Center (SFAB), Tongji Architectural Design (Group) Co., Ltd. 

Key words: artificial intelligence, building information modeling, evacuation, design automation, deep Q-learning 

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