Research on Intelligent Building Design Optimization Method Based on BIM and AI Integration
- DOI
- 10.2991/978-94-6463-793-9_56How to use a DOI?
- Keywords
- building information modeling; artificial intelligence; spatial layout optimization; energy performance optimization; human-computer interaction
- Abstract
Aiming at the problems of inefficiency and difficult collaboration of traditional building design methods, this paper proposes BIAO, an intelligent building design optimization framework integrated with BIM and AI, which realizes the effective conversion of BIM data through the hierarchical information extraction engine, and builds a full-process intelligent method from the data layer to the decision-making layer by combining the multi-objective optimization algorithms and the visualization interactive system. A two-way optimization technology of space layout and energy performance adapted to different building types is developed, realizing the automation and intelligence of the design process. The framework application test verifies that the method has significant design efficiency improvement and performance optimization effect, and provides a feasible technical path for the digital transformation of building design.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Gao Lv AU - Qiaoqiao Zeng PY - 2025 DA - 2025/07/28 TI - Research on Intelligent Building Design Optimization Method Based on BIM and AI Integration BT - Proceedings of the 2025 8th International Conference on Traffic Transportation and Civil Architecture (ICTTCA 2025) PB - Atlantis Press SP - 658 EP - 670 SN - 2589-4943 UR - https://doi.org/10.2991/978-94-6463-793-9_56 DO - 10.2991/978-94-6463-793-9_56 ID - Lv2025 ER -