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Fire
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Fire Risk Management and Emergency Prevention
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Fire Risk Management and Emergency Prevention

A special issue of Fire (ISSN 2571-6255).

Deadline for manuscript submissions: 31 January 2027 | Viewed by 9583

Editors

Dr. Xuecai Xie

E-Mail Website
Guest Editor
College of Engineering and Technology, China University of Geosciences, Beijing, China
Interests: modern safety management; intelligent firefighting; risk assessment and warning; emergency prevention
Dr. Xueming Shu

E-Mail Website
Guest Editor
School of Safety Science, Tsinghua University, Beijing, China
Interests: intelligent firefighting; risk assessment and warning; emergency prevention; social governance
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Haowei Yao

E-Mail Website
Guest Editor
1. College of Building Environment Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China
2. Zhengzhou Key Laboratory of Electric Power Fire Safety, Zhengzhou University of Light Industry, Zhengzhou 450001, China
Interests: electrical fire; building fire; new energy fire
Special Issues, Collections and Topics in MDPI journals
Dr. Jun Hu

E-Mail Website
Guest Editor
School of National Safety and Emergency Management, Beijing Normal University, Beijing, China
Interests: fire risk assessment; fire warning; intelligent firefighting

Special Issue Information

Dear Colleagues,

Fire accidents have always been a matter of great concern to people, and their prevention and rescue are significant to everyone. In 2024, there were 908000 fires nationwide, resulting in 2001 deaths and an economic loss of 7.74 billion RMB. Fire risk management and emergency prevention are complex and comprehensive tasks. In the era of digital intelligence, the development of technologies such as artificial intelligence and big data has provided many new technologies for fire prevention. In order to better manage fire risks and prevent emergencies, this Special Issue will be launched. We are pleased to invite you to follow this Special Issue and submit your latest manuscript.

This Special Issue aims to theories, technologies, and equipment related to fire risk management and emergency prevention, particularly digital and intelligent technologies and methods. The scope covers residential fires, commercial building fires, industrial park fires, and more.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

(1) The theory and methods of fire risk management.

(2) The theory and methods of fire risk assessment.

(3) Technologies and equipment for fire risk prediction.

(4) Technologies and equipment for fire emergency prevention.

(5) Digital technology for fire prevention and rescue.

(6) Fire accident prevention safety training and emergency science popularization.

(7) Intelligent technology and equipment for fire prevention.

(8) Fire prevention and extinguishing technology

(9) Research related to fire risk management and emergency prevention

(10) AI driven fire surveillance technologies and embodied Intelligence

We look forward to receiving your contributions.

Dr. Xuecai Xie
Dr. Xueming Shu
Prof. Dr. Haowei Yao
Dr. Jun Hu
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 risk analysis
  • fire risk assessment
  • fire risk warning
  • fire risk management
  • fire emergency rescue
  • technology and equipment
  • digital technology

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  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (7 papers)

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Research

19 pages, 20790 KB  
Article
Coal Spontaneous Oxidation Mechanism of Low-Molecular Compounds: Pentanol
by Tianyi Yang, Xiaobo Wang, Wenhao Deng, Sichen Liu, Hanzhong Deng and Yafei Shan
Fire 2026, 9(6), 253; https://doi.org/10.3390/fire9060253 - 13 Jun 2026
Viewed by 636
Abstract
Coal spontaneous combustion (CSC) remains a major hazard in coal mining. Research on CSC has largely focused on macromolecular structures, while the behavior of low-molecular-weight compounds remains unclear. Using B3LYP/6-311G density functional theory, this study systematically reveals thirteen microscopic reaction pathways, active sites, [...] Read more.
Coal spontaneous combustion (CSC) remains a major hazard in coal mining. Research on CSC has largely focused on macromolecular structures, while the behavior of low-molecular-weight compounds remains unclear. Using B3LYP/6-311G density functional theory, this study systematically reveals thirteen microscopic reaction pathways, active sites, and the energy barrier order of pentanol during coal spontaneous combustion. The oxidation proceeds via thirteen multi-step pathways involving bond breaking and formation, with the dominant reaction being oxygen attack on the -CH2OH group to produce pentanal (CH3CH2CH2CH2CHO) and water as the main products. The priority order of thirteen reaction pathways between pentanol and oxygen was established as: Path 6 > Path 3 > Path 8 > Path 5 > Path 4 > Path 1 > Path 11 > Path 10 > Path 9 > Path 12 > Path 7 > Path 2. The results reveal the multi-step bond-breaking and formation mechanism at the molecular level, providing a fundamental theoretical framework for understanding the radical chain oxidation mechanism of low molecular weight compounds in CSC. Full article
(This article belongs to the Special Issue Fire Risk Management and Emergency Prevention)
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17 pages, 26220 KB  
Article
Oxidation Mechanisms of Low Molecular Valeric Acidic Compounds in Coal Spontaneous Combustion
by Shaobo Qu, Xiaobo Wang, Tianyi Yang, Wenhao Deng, Sichen Liu, Hanzhong Deng, Yafei Shan and Hongguang Ji
Fire 2026, 9(6), 237; https://doi.org/10.3390/fire9060237 - 3 Jun 2026
Viewed by 530
Abstract
Coal spontaneous combustion seriously threatens the safety of coal mine production, and studies on low molecular compounds in coal spontaneous combustion are limited. The chemical reaction process of low molecular compound valeric acid in coal spontaneous combustion was studied using B3LYP/6-311G quantum chemical [...] Read more.
Coal spontaneous combustion seriously threatens the safety of coal mine production, and studies on low molecular compounds in coal spontaneous combustion are limited. The chemical reaction process of low molecular compound valeric acid in coal spontaneous combustion was studied using B3LYP/6-311G quantum chemical density functional theory. The mechanism of valeric acid in coal spontaneous combustion was disclosed, which is the process of chemical bond formation and breakage. The findings implied that the active sites of valeric acid during combustion are C1, C5, C8, and C11 atoms. Twelve reaction channels have also been theoretically determined in the following order: Path6 > Path3 > Path8 > Path5 > Path4 > Path1 > Path11 > Path10 > Path9 > Path12 > Path7 > Path2. This is significant for developing low molecular spontaneous combustion inhibitors and preventing coal spontaneous combustion. Full article
(This article belongs to the Special Issue Fire Risk Management and Emergency Prevention)
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28 pages, 4780 KB  
Article
Retrieval over Response: Large Language Model-Augmented Decision Strategies for Hierarchical Wildfire Risk Evaluation
by Yuheng Cheng, Yuchen Lin, Yanwei Wu, Lida Huang, Tao Chen, Wenguo Weng and Xiaole Zhang
Fire 2026, 9(4), 143; https://doi.org/10.3390/fire9040143 - 26 Mar 2026
Cited by 1 | Viewed by 1745
Abstract
The Analytic Hierarchy Process (AHP) is widely used in Multi-Criteria Decision Analysis (MCDA), yet its strong reliance on expert judgment constrains its scalability and may introduce variability in weighting outcomes, particularly in high-stakes applications such as wildfire risk assessment. In this study, we [...] Read more.
The Analytic Hierarchy Process (AHP) is widely used in Multi-Criteria Decision Analysis (MCDA), yet its strong reliance on expert judgment constrains its scalability and may introduce variability in weighting outcomes, particularly in high-stakes applications such as wildfire risk assessment. In this study, we investigate how Large Language Models (LLMs) can function as decision-support agents in an AHP-style hierarchical evaluation task derived from validated wildfire literature. Based on this structure, four representative LLM-assisted strategies are examined: Direct LLM Scoring (DLS), Multi-Model Debate Scoring (MDS), Full-Document Prompting (FDP), and Indicator-Guided Prompting (IGP). To evaluate their effectiveness, we benchmark LLM-generated rankings against expert-defined ground truth across 16 sub-criteria. Using the mean correlation coefficient R as the key evaluation metric, with reported values expressed as mean ± standard deviation across models: DLS shows no correlation with expert rankings (R = 0.009 ± 0.070), MDS yields marginal gains (R = 0.181), and FDP remains unstable (R = 0.081 ± 0.189). By contrast, IGP, which incorporates retrieval-informed structured prompting, shows the highest agreement with the expert reference among the four compared strategies (R = 0.598 ± 0.065), suggesting that structured contextual guidance may improve the performance of LLM-assisted weighting within the evaluated benchmark. This study suggests that, within the evaluated wildfire benchmark and the tested set of hosted LLMs, LLMs may serve as useful decision-support tools in MCDA tasks when guided by structured inputs or coordinated through multi-agent mechanisms. The proposed framework provides an interpretable basis for exploring LLM-assisted risk evaluation in the present wildfire benchmark, while further validation is needed before extending it to other environmental or safety-critical contexts. Full article
(This article belongs to the Special Issue Fire Risk Management and Emergency Prevention)
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18 pages, 1050 KB  
Article
Research on Fire Smoke Recognition Algorithm with Image Enhancement for Unconventional Scenarios in Under-Construction Nuclear Power Plants
by Tingren Wang, Guangwei Liu, Kai Yu and Baolin Yao
Fire 2026, 9(3), 128; https://doi.org/10.3390/fire9030128 - 17 Mar 2026
Viewed by 1117
Abstract
Accurate identification of fire smoke is a key link in realizing early fire prevention and control. Traditional intelligent video and image processing technologies are significantly restricted by environmental factors, with weak anti-interference capabilities and limitations in distinguishing fire smoke, leading to a high [...] Read more.
Accurate identification of fire smoke is a key link in realizing early fire prevention and control. Traditional intelligent video and image processing technologies are significantly restricted by environmental factors, with weak anti-interference capabilities and limitations in distinguishing fire smoke, leading to a high false alarm rate of fires. To address this problem, this paper proposes an unconventional visual field smoke detection method based on image enhancement. The method innovatively improves the Retinex algorithm by integrating improved guided filtering, adaptive brightness correction, and CLAHE-WWGIF joint processing, which realizes targeted optimization for the unique interference factors of under-construction nuclear power plants such as water mist, low illumination, and equipment occlusion. First, an improved Retinex algorithm is used to process the image to improve the image brightness and contrast, retain edge details while avoiding halo artifacts, reduce the impact of noise, and optimize visual features. Then, the sample data set is integrated, and the YOLOv11 target detection algorithm is used to achieve accurate identification and positioning of smoke targets. Experimental data shows that the fire identification method achieves an accuracy rate of 93.6% and 92.3% for fire smoke identification in interference-prone scenarios such as dark nights and water mist, respectively, and the response time to fire smoke is only 1.8 s and 2.1 s. In practical on-site applications at nuclear power plant construction sites, the method is integrated into an “edge computing + distributed deployment” hardware system, which realizes real-time smoke detection in core areas such as nuclear islands and conventional islands with a false alarm rate of less than 5% and a detection delay of ≤300 ms, meeting the ultra-strict safety monitoring requirements of nuclear power projects. Experiments show that this method can be effectively applied to smoke detection scenarios under unconventional visual fields, accurately identify smoke, provide reliable technical support for fire smoke identification under unconventional visual fields, significantly reduce the false alarm rate of fire detection, and provide technical support for the safety of under-construction nuclear power plants. Full article
(This article belongs to the Special Issue Fire Risk Management and Emergency Prevention)
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20 pages, 1821 KB  
Article
Research on AI-Assisted Fire Risk Target Detection for Special Operating Conditions in Under-Construction Nuclear Power Plants
by Zhendong Li, Guangwei Liu, Kai Yu and Shijie Du
Fire 2026, 9(3), 115; https://doi.org/10.3390/fire9030115 - 3 Mar 2026
Viewed by 1230
Abstract
In night-time construction scenarios of under-construction nuclear power plants, some yellow lights and open flames exhibit highly similar visual characteristics, resulting in frequent false alarms of fire sources. Such false alarm information tends to drown out real fire alarm signals, which not only [...] Read more.
In night-time construction scenarios of under-construction nuclear power plants, some yellow lights and open flames exhibit highly similar visual characteristics, resulting in frequent false alarms of fire sources. Such false alarm information tends to drown out real fire alarm signals, which not only severely disrupts construction operations but also endangers fire safety. To address this problem, this paper proposes an intelligent fire risk identification method based on an enhanced YOLOv8n (named YOLO-Fire). Specifically, shallow convolutional layers embedded with a coordinate attention mechanism are integrated into the Backbone of YOLOv8n; the Neck is optimised to improve the efficiency of multi-scale feature fusion; and the Head is enhanced to strengthen the localization and classification branches. Additionally, a composite loss function combining classification loss, regression loss, and similarity loss is designed, coupled with night-scene-specific data augmentation techniques and a two-stage progressive training strategy. Experimental results show that YOLO-Fire reduces the false alarm rate by 14.3%, increases the mean average precision (mAP@0.5) for open flames by 11.3% to 75.2%, and maintains an inference speed of over 85 frames per second (FPS). This study achieves an optimal balance between false alarm control, small object detection accuracy, and real-time processing efficiency, effectively resolving the misclassification issue between open flames and lights in night-time construction scenarios, and providing precise and efficient intelligent technical support for fire risk prevention and control during the construction phase of nuclear power plants. Full article
(This article belongs to the Special Issue Fire Risk Management and Emergency Prevention)
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21 pages, 5653 KB  
Article
A Deep Learning Framework for Real-Time Prediction of Thermal and Structural Responses in Car Park Fires
by Xiqiang Wu, Yuanpeng Gao, Wen Xiong and Chunsheng Cai
Fire 2025, 8(12), 470; https://doi.org/10.3390/fire8120470 - 2 Dec 2025
Viewed by 1490
Abstract
Car parks are a vital component of infrastructure in modern cities. However, fire in car park buildings may lead to significant structural damage and casualties, highlighting the urgent need for fast forecasting methods. Traditional simulation methods are computationally prohibitive for immediate decision-making during [...] Read more.
Car parks are a vital component of infrastructure in modern cities. However, fire in car park buildings may lead to significant structural damage and casualties, highlighting the urgent need for fast forecasting methods. Traditional simulation methods are computationally prohibitive for immediate decision-making during a fire incident. This study develops a unified deep learning architecture for a real-time prediction of both the temperature distribution and structural response in car park fires. A numerical database was established using FDS and Abaqus, considering key variables including fire size, fire location and load level. A deep learning model based on the convolutional neural network and long short-term memory networks was proposed. The model takes a 10 s history of gas temperatures from ceiling sensors and the applied load level as input to give predictions on the spatial temperature distribution at a 2 m height 3 min into the future and the vertical deflection of the slab edge for up to 5 h after fire ignition. The model achieved high accuracy, with R2 values of 92% for temperature prediction and 95% for deflection prediction. This study provides a new approach for real-time fire and structural safety early warning. Full article
(This article belongs to the Special Issue Fire Risk Management and Emergency Prevention)
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24 pages, 14992 KB  
Article
Fire Prevention in Traditional Dwellings of Southern Hunan: A Case Study of Zhoujia Compound
by Xian Guan, Liang Xie, Enping Guo and Yanxiang Chen
Fire 2025, 8(11), 416; https://doi.org/10.3390/fire8110416 - 28 Oct 2025
Cited by 1 | Viewed by 1729
Abstract
This study presents a fire risk assessment of traditional wooden dwellings in Southern Hunan, focusing on Zhoujia Compound—a nationally protected cultural heritage site. By applying Pyrosim fire simulation software, we modeled fire spread, smoke dispersion, and temperature variation under localized architectural and environmental [...] Read more.
This study presents a fire risk assessment of traditional wooden dwellings in Southern Hunan, focusing on Zhoujia Compound—a nationally protected cultural heritage site. By applying Pyrosim fire simulation software, we modeled fire spread, smoke dispersion, and temperature variation under localized architectural and environmental conditions. The simulations, informed by real-time wind speed monitoring, revealed that key fire risks stem from open flame activities during festivals, charcoal heating, and inadequate electrical wiring. Structural features such as interconnected wooden beams and open courtyards exacerbate fire spread. The results identified high-risk zones and demonstrated that wind speed and building orientation significantly affect fire dynamics. Based on these findings, we propose targeted fire prevention strategies, including fire-retardant treatments, improved compartmentalization, and community-level fire education. This research offers a novel, simulation-based approach to improving fire safety in traditional villages, contributing to both cultural heritage protection and rural fire risk mitigation. Full article
(This article belongs to the Special Issue Fire Risk Management and Emergency Prevention)
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