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Fire
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Building Fire Safety and Intelligent Protection Technologies
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Building Fire Safety and Intelligent Protection Technologies

A Special Issue of Fire (ISSN 2571-6255) belonging to the section "Fire Risk Assessment and Safety Management in Buildings and Urban Spaces".

Deadline for manuscript submissions: 31 May 2027 | Viewed by 1196

Editors

Dr. Xuehong Gao

E-Mail Website
Guest Editor
Research Institute of Macro-Safety Science, University of Science and Technology Beijing, Beijing 100083, China
Interests: artificial intelligence; optimization; emergency managerment; disaster response
Dr. Zhengqing Zhou

E-Mail Website
Guest Editor
Research Institute of Macro-Safety Science, University of Science and Technology Beijing, Beijing 100083, China
Interests: hydrogen fluoride leakage and diffusion based on CFD simulation; ventilation; chemical fire
Dr. Yang Li

E-Mail Website
Guest Editor
Forensic Science Institute, China People’s Police University, Langfang 065000, China
Interests: fire investigation; electrical fires
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Focusing on the theme "Building Fire Safety and Intelligent Protection Technologies", this Special Issue addresses a central challenge in modern fire safety science and engineering: integrating established core fire safety disciplines with cutting-edge intelligent technologies to create resilient building environments. The urgency of this integration is starkly highlighted by the catastrophic 2025 fire at Wang Fuk Court (宏福苑) in Hong Kong, a tragedy that exposed profound vulnerabilities in traditional safety management for high-density urban living. In response, initiatives such as Hong Kong's pilot programs for IoT-based fire detection systems are already underway.

We therefore invite expert contributions that actively bridge these domains. We seek research connecting deep expertise in core fire safety disciplines—including enclosure and façade fire dynamics, smoke movement and control, structural and material fire resistance, suppression, and evacuation—with advanced intelligent solutions such as AI-driven detection, IoT monitoring, automated suppression, and digital twins.

By fostering this interdisciplinary dialogue, we aim to build robust collaborations and accelerate the development of next-generation, intelligent fire safety frameworks for buildings worldwide.

Dr. Xuehong Gao
Dr. Zhengqing Zhou
Dr. Yang Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fire is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fire dynamics (enclosure and façade)
  • smoke movement & control
  • fire resistance (structural & building material)
  • fire suppression (sprinkler, water mist, gas, etc.)
  • performance-based fire design
  • emergency evacuation and human behavior
  • fire investigation
  • smart fire detection
  • intelligent evacuation technology
  • intelligent fire suppression
  • intelligent fire safety management
  • artificial intelligence (AI)
  • Internet of Things (IoT)
  • digital twin
  • building fire risk assessment
  • drone fire extinguishing
  • robot rescue

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

31 pages, 11252 KB  
Article
A Novel Robust HL-Based Transformer Approach for Predicting Electrical Fire Risks
by Guozhong Huang, Yaohui Shen, Ciai Tang, Qiuhang Wu, Huiling Jiang and Xuehong Gao
Fire 2026, 9(6), 244; https://doi.org/10.3390/fire9060244 - 7 Jun 2026
Viewed by 796
Abstract
Accurate prediction of electrical fire risk is important for early warning, but real-world monitoring data are often affected by sensor noise, transient anomalies, and non-Gaussian interference. This study proposes an HL-Transformer that incorporates an HL-Pooling layer based on the Hodges–Lehmann estimator into the [...] Read more.
Accurate prediction of electrical fire risk is important for early warning, but real-world monitoring data are often affected by sensor noise, transient anomalies, and non-Gaussian interference. This study proposes an HL-Transformer that incorporates an HL-Pooling layer based on the Hodges–Lehmann estimator into the Transformer feature aggregation process. The HL-Pooling layer replaces conventional mean- or max-based pooling by using the median of pairwise averages, aiming to suppress abnormal perturbations while preserving temporal information. Experiments were conducted on a real-world electrical fire monitoring dataset and the public ETTh1 dataset, with additional robustness tests under different outlier ratios and intensities. The results show that, within the same Transformer backbone, HL-Transformer reduced the MSE by 75.4% compared with the Max-Pooling variant and achieved an R2 of 0.879 on the electrical fire risk prediction task. Under injected outliers, the HL-Pooling layer showed more stable error trends, and its transfer to TCN, CNN-LSTM, and 1D-CNN models further improved predictive performance. These findings indicate that HL-Pooling is a robust and portable alternative to conventional pooling for time-series forecasting in noisy monitoring environments. Full article
(This article belongs to the Special Issue Building Fire Safety and Intelligent Protection Technologies)
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