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.

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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
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article

Generalized optimization method for fire evacuation routes in historic dense settlements based on improved ant colony algorithm and BIM

Jingjing Qiu, Yuan Liu, Jiantuan Qin, Juanjuan Qin et al.
Discover Artificial Intelligence
Evacuation and Crowd Dynamics
article

Generalized optimization method for fire evacuation routes in historic dense settlements based on improved ant colony algorithm and BIM

Jingjing Qiu, Yuan Liu, Jiantuan Qin, Juanjuan Qin, Yingli Hu
article en

Abstract

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.

Discover Artificial IntelligenceVol. 6(1)
Guangxi University of Science and Technology (CN), Beijing Urban Construction Design & Development Group (China) (CN), Guangxi Research Institute of Chemical Industry (CN)
Sustainable cities and communities
Openalex Percentile: Top 15%
Evacuation and Crowd Dynamics
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