Generalized optimization method for fire evacuation routes in historic dense settlements based on improved ant colony algorithm and BIM
This study combines Building Information Modeling (BIM) with an improved ant colony optimization (IACO) algorithm to optimize evacuation paths during fires in historic dense settlements. A 3D village model was created using Revit, and fire simulations provided data on temperature and smoke concentration. The IACO algorithm, enhanced with a backtracking strategy, improves convergence speed and global search ability.Simulation results show the optimal evacuation route is shortened from 17.75 m (ACO) to 15.11 m (IACO), with a maximum 14.9% reduction in true physical evacuation distance, all results are mean ± standard deviation ( n = 25 independent random seeds). The proposed BIM-IACO method forms a replicable technical framework validated on Ma’an Village. Further verification on structurally different heritage settlements is required to expand its applicable scope, providing targeted fire evacuation planning support for brick-wood ancient villages with narrow alleys.
Authors
- Jingjing Qiu (ORCID: https://orcid.org/0000-0001-9643-5452)
- Yuan Liu
- Jiantuan Qin (ORCID: https://orcid.org/0009-0000-7606-7672)
- Juanjuan Qin
- Yingli Hu
Institutions
- Guangxi University of Science and Technology (CN)
- Beijing Urban Construction Design & Development Group (China) (CN)
- Guangxi Research Institute of Chemical Industry (CN)
Publication Details
- Journal
- Discover Artificial Intelligence
- Published
- 2026-09-22
- DOI
- https://doi.org/10.1007/s44163-026-02256-2
- Primary Topic
- Evacuation and Crowd Dynamics
- Type
- article
- Field-Weighted Citation Impact
- 0.00