Development of a Tenability-based Path Planning Model for Building Fire Evacuations Using Reinforcement Learning

Published

December 30, 2025

Author(s)

Hongqiang Fang, Wai Cheong Tam, Ruggiero Lovreglio, Md. Ismail Siddiqi Emon, Michael Xuelin Huang

Abstract

To enhance the safety of building fire evacuations by accounting for the dynamic and cumulative impacts of hazardous fire conditions on evacuees, we developed SafeStep, a novel reinforcement learning-based path planning model that integrates occupant tenability into evacuation decision-making. Specifically, the model employs the Fractional Effective Dose (FED) of toxic gases to quantify dynamic fire impacts, and a FED-derived reward function is formulated to guide the RL agent toward safer and more efficient paths. A Deep Q-Network (DQN) is adopted to optimize evacuation path planning within complex building geometries. SafeStep is benchmarked against traditional path planning algorithms, Dijkstra's algorithm (DA), through two test cases. In one case, the results show that SafeStep provides safer evacuation paths, achieving approximately a 62% reduction in FED exposure compared to DA. In the other, it successfully identifies viable evacuation routes in scenarios where the DA-based model fails. To further assess its applicability, a case study in a complex building geometry shows that SafeStep can consistently generate evacuation paths with lower FED across arbitrary starting positions. These findings indicate that SafeStep effectively addresses key limitations of traditional path planning algorithms, which often fail to account for evolving fire dynamics and cumulative fire effects. As such, the proposed model has strong potential to support smart building technologies, such as dynamic directional exit signs, to enhance evacuation safely and efficiently in real-world fire emergencies.

Citation

Journal of Building Engineering

Pub Type

Journals

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Keywords

smart evacuation, emergency response, Deep Q-learning, evacuees’ tenability, dynamic directional exit signs

Citation

FANG, H. , Tam, W. , Lovreglio, R. , Emon, M. and Huang, M. (2025), Development of a Tenability-based Path Planning Model for Building Fire Evacuations Using Reinforcement Learning, Journal of Building Engineering, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=960272 (Accessed April 3, 2026)