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Fire
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Relevance and Applicability of AI for Fire Engineering
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Relevance and Applicability of AI for Fire Engineering

A Special Issue of Fire (ISSN 2571-6255).

Deadline for manuscript submissions: 28 February 2027 | Viewed by 2863

Editors

Dr. Xiaolei Zhang

E-Mail Website
Guest Editor
State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, China
Interests: fire dynamics; buoyant flow dynamics; fire plume entrainment; combustion; flame spread behavior; heat transfer; fire modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Jiang Lv

E-Mail Website
Guest Editor
State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, China
Interests: diffusion jet flame; fire plume; flame instability behavior; hypergravity flame behavior
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As artificial intelligence technologies continue to advice, fire engineering, like most other disciplines, is undergoing a transformative shift. AI has an unparalleled capacity for fire detection, predictive modeling of fire dynamics, intelligent evacuation systems, and structural fire response analysis—areas which are critical for enhancing public safety and infrastructure resilience. As such, we are pleased to invite you to contribute to the forthcoming Special Issue "Relevance and Applicability of AI for Fire Engineering."

Our aim is to showcase how AI enhances understanding, prediction, and management of fire hazards, with particular focus on fire science, safety engineering, and hazard mitigation.

Both original research articles and reviews are welcome. Research topics may include (but are not limited to) the following:

-Using AI for fire dynamics simulation.

-Fire risk prediction models.

-Intelligent fire monitoring and early warning.

-AI applications in new energy fire safety.

-Using machine learning for fire behavior prediction and smoke spread modeling.

-Using computer vision for fire scene analysis.

-Data-driven fire risk assessment methodologies.

We look forward to receiving your contributions.

Dr. Xiaolei Zhang
Dr. Jiang Lv
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

  • artificial intelligence
  • fire engineering
  • fire safety
  • data-driven modeling
  • machine learning
  • fire dynamics
  • evacuation optimization
  • risk assessment
  • sensor fusion
  • emergency response

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

Published Papers (3 papers)

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Research

14 pages, 3727 KB  
Article
Research on Aircraft Fire Detection Method Based on IATF-YOLO
by Wei Zhang, Kai Wang and Xiaosong Song
Fire 2026, 9(6), 255; https://doi.org/10.3390/fire9060255 - 15 Jun 2026
Viewed by 772
Abstract
Aircraft cargo compartment fires constitute a significant type of aviation fire, posing a grave threat to aviation safety. To guard against and respond to such fires, existing aircraft cargo compartments are equipped with smoke detection fire detectors, which rely on perceiving changes in [...] Read more.
Aircraft cargo compartment fires constitute a significant type of aviation fire, posing a grave threat to aviation safety. To guard against and respond to such fires, existing aircraft cargo compartments are equipped with smoke detection fire detectors, which rely on perceiving changes in smoke transmittance to determine the onset of a fire. However, these detectors offer relatively low recognition accuracy and cannot provide a direct visual representation of the fire. In this work, we introduce a fire recognition method built on image sensors and a deep learning model. In light of the irregular shapes of flames and smoke, an improved interactive triplet attention mechanism (ITAM) is integrated into the You Only Look Once version 5 (YOLOv5) model, enhancing the model’s recognition accuracy. Furthermore, the original Neck structure is replaced with an Asymptotic Feature Pyramid Network (AFPN), improving the model’s ability to recognize small targets, which is particularly useful for detecting flames and smoke early in a fire. This paper further improves the model’s recognition accuracy by introducing the Focaler-IoU loss function, which balances the feature learning of hard and easy samples. Therefore, the network model in this paper is named IATF-YOLO. Ablation experiments demonstrate that our algorithm improves accuracy by 2%, while comparative experiments with several mainstream baseline models show that our algorithm achieves a 0.7% accuracy improvement, with a final peak accuracy of 93.6%. Full article
(This article belongs to the Special Issue Relevance and Applicability of AI for Fire Engineering)
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16 pages, 6205 KB  
Article
Research on Characteristic Analysis of Typical Fire Accidents and Trend Prediction of Workplace Accidents
by Fangming Xue, Binbin Wu, Jiawei Ding, Chao Wang, Wei Ding and Fei Ren
Fire 2026, 9(6), 229; https://doi.org/10.3390/fire9060229 - 1 Jun 2026
Viewed by 994
Abstract
Workplace accidents pose a serious threat to people’s lives and property and hinder social and economic development. Among these accidents, fire accidents are typical due to their sudden occurrence and severe consequences. To better understand accident evolution laws and improve risk prevention, this [...] Read more.
Workplace accidents pose a serious threat to people’s lives and property and hinder social and economic development. Among these accidents, fire accidents are typical due to their sudden occurrence and severe consequences. To better understand accident evolution laws and improve risk prevention, this study analyzes the characteristics of typical national fire accidents based on 2015–2024 accident statistics. A linear-nonlinear combined Autoregressive Integrated Moving Average-Long Short-Term Memory (ARIMA-LSTM) model is established to predict trends of the number of national overall workplace accidents, deaths, injuries, and direct economic losses, and it is compared with Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Seasonal Autoregressive Integrated Moving Average-Long Short-Term Memory (SARIMA-LSTM) models. The results show that the ARIMA-LSTM model integrates the strengths of linear fitting and nonlinear learning, with stronger explanatory power and higher prediction accuracy, as reflected by lower Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) values. This study provides technical support for the precise prevention and control of fire accidents, trend prediction of work safety accidents, and helps to establish a scientific and forward-looking safety risk prevention and control system. Full article
(This article belongs to the Special Issue Relevance and Applicability of AI for Fire Engineering)
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17 pages, 14756 KB  
Article
FlameDet: A Computational Framework for Flame Detection via Physics-Inspired Heat Diffusion and Multi-Resolution Frequency Analysis
by Song Han, Fei Ren, Chenglin Liu, Zekang Zhang, Tianzhu Wang, Jiayin Liu and Honglei Che
Fire 2026, 9(6), 223; https://doi.org/10.3390/fire9060223 - 28 May 2026
Viewed by 661
Abstract
The development of robust and efficient computational methodologies is crucial for vision-based flame detection systems. While deep learning has shown promise, many existing models are direct applications of generic architectures, lacking principled methodologies to address the inherent physical characteristics of flame, such as [...] Read more.
The development of robust and efficient computational methodologies is crucial for vision-based flame detection systems. While deep learning has shown promise, many existing models are direct applications of generic architectures, lacking principled methodologies to address the inherent physical characteristics of flame, such as heat diffusion and multi-scale radiative patterns. To bridge this gap, this paper proposes FlameDet, a novel flame detection framework grounded in physics-inspired computing and multi-resolution analysis. Unlike conventional approaches, FlameDet formulates visual feature propagation through the lens of heat conduction physics. The core contribution is the Heat Diffusion Module, a computationally efficient backbone that explicitly models feature spread by solving a parameterized heat equation via discrete cosine transform. This physics-aligned design achieves a global receptive field with O(N1.5) complexity, processing high-resolution inputs 2× faster with 54% less memory than RT-DETR-ResNet50, while providing an interpretable computational process. Furthermore, a High–Low Frequency Analysis module is proposed, a multi-resolution computational strategy that decomposes features into low-frequency components for global context and high-frequency components for fine-grained details. To enhance contextual reasoning for small flames without computational penalty, a DSK_C3 module that employs dilated convolutions and structural re-parameterization is designed, expanding the receptive field and by 26.5%. Extensive experiments on FlameLife dataset demonstrate that FlameDet establishes a new state-of-the-art, improving the F1-score and AP50 by 3.5% and 4.0%, respectively, while maintaining superior efficiency. Full article
(This article belongs to the Special Issue Relevance and Applicability of AI for Fire Engineering)
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