Emergency Rescue Technology For Building Fire And Industrial Thermal Disaster: A Comprehensive Review
Building fires and industrial thermal disasters rank among the most devastating hazards confronting modern societies, causing thousands of fatalities, billions of dollars in economic losses, and lasting environmental damage each year. The increasing complexity of built environments—including high-rise structures, underground facilities, and chemical processing plants—demands a commensurate advancement in emergency rescue technologies. This review provides a comprehensive examination of state-of-the-art technologies employed in the detection, suppression, and rescue operations associated with building fires and industrial thermal disasters. Key domains covered include early fire detection systems integrating the Internet of Things (IoT), artificial intelligence (AI), and machine learning; advanced fire suppression technologies such as water mist, gaseous agents, and high-expansion foam; autonomous and semi-autonomous rescue robotics; unmanned aerial vehicles (UAVs) equipped with thermal imaging; Building Information Modeling (BIM) and digital twins for emergency planning and evacuation simulation; personal protective equipment (PPE) advancements including biometric wearables; and communication and situational-awareness systems. Special attention is given to industrial thermal hazards, including petrochemical fires, hazardous material incidents, BLEVE events, and dust explosions. The paper concludes with a critical synthesis of current research gaps and recommendations for future work, emphasizing integrated, data-driven, and human-centered approaches to emergency rescue.
Authors
- Zhixiang Xing (ORCID: https://orcid.org/0000-0002-0191-1733)
- Mobinul Haque (ORCID: https://orcid.org/0009-0005-7240-8683)
Institutions
- Changzhou University (CN)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-09-30
- DOI
- https://doi.org/10.5281/zenodo.23053830
- Primary Topic
- Fire Detection and Safety Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00