Fire | August 2025 - Browse Articles

30 pages, 1966 KiB

Open AccessArticle

Effective Comparison of Thermo-Mechanical Characteristics of Self-Compacting Concretes Through Machine Learning-Based Predictions

by Armando La Scala and Leonarda Carnimeo

Fire 2025, 8(8), 289; https://doi.org/10.3390/fire8080289 - 23 Jul 2025

Abstract

This present study proposes different machine learning-based predictors for the assessment of the residual compressive strength of Self-Compacting Concrete (SCC) subjected to high temperatures. The investigation is based on several literature algorithmic approaches based on Artificial Neural Networks with distinct training algorithms (Bayesian Regularization, Levenberg–Marquardt, Scaled Conjugate Gradient, and Resilient Backpropagation), Support Vector Regression, and Random Forest methods. A training database of 150 experimental data points is derived from a careful literature review, incorporating temperature (20–800 °C), geometric ratio (height/diameter), and corresponding compressive strength values. A statistical analysis revealed complex non-linear relationships between variables, with strong negative correlation between temperature and strength and heteroscedastic data distribution, justifying the selection of advanced machine learning techniques. Feature engineering improved model performance through the incorporation of quadratic terms, interaction variables, and cyclic transformations. The Resilient Backpropagation algorithm demonstrated superior performance with the lowest prediction errors, followed by Bayesian Regularization. Support Vector Regression achieved competitive accuracy despite its simpler architecture. Experimental validation using specimens tested up to 800 °C showed a good reliability of the developed systems, with prediction errors ranging from 0.33% to 23.35% across different temperature ranges. Full article

17 pages, 3457 KiB

Open AccessArticle

Lightweight UAV-Based System for Early Fire-Risk Identification in Wild Forests

by Akmalbek Abdusalomov, Sabina Umirzakova, Alpamis Kutlimuratov, Dilshod Mirzaev, Adilbek Dauletov, Tulkin Botirov, Madina Zakirova, Mukhriddin Mukhiddinov and Young Im Cho

Fire 2025, 8(8), 288; https://doi.org/10.3390/fire8080288 - 23 Jul 2025

Abstract

The escalating impacts and occurrence of wildfires threaten the public, economies, and global ecosystems. Physiologically declining or dead trees are a great portion of the fires because these trees are prone to higher ignition and have lower moisture content. To prevent wildfires, hazardous vegetation needs to be removed, and the vegetation should be identified early on. This work proposes a real-time fire risk tree detection framework using UAV images, which is based on lightweight object detection. The model uses the MobileNetV3-Small spine, which is optimized for edge deployment, combined with an SSD head. This configuration results in a highly optimized and fast UAV-based inference pipeline. The dataset used in this study comprises over 3000 annotated RGB UAV images of trees in healthy, partially dead, and fully dead conditions, collected from mixed real-world forest scenes and public drone imagery repositories. Thorough evaluation shows that the proposed model outperforms conventional SSD and recent YOLOs on Precision (94.1%), Recall (93.7%), mAP (90.7%), F1 (91.0%) while being light-weight (8.7 MB) and fast (62.5 FPS on Jetson Xavier NX). These findings strongly support the model’s effectiveness for large-scale continuous forest monitoring to detect health degradations and mitigate wildfire risks proactively. The framework UAV-based environmental monitoring systems differentiates itself by incorporating a balance between detection accuracy, speed, and resource efficiency as fundamental principles. Full article

18 pages, 7406 KiB

Open AccessArticle

Deep-Learning-Driven Technique for Accurate Location of Fire Source in Aircraft Cargo Compartment

by Yulong Zhu, Changzheng Li, Shupei Tang, Xuhong Jia, Xia Chen, Quanyi Liu and Wan Ki Chow

Fire 2025, 8(8), 287; https://doi.org/10.3390/fire8080287 - 23 Jul 2025

Abstract

Accurate fire source location in an aircraft cargo compartment cannot be determined by common design practices. This study proposes an advanced fire location inversion framework based on a Convolutional Long-Short-Term Memory (ConvLSTM) network. A self-designed interpolation preprocessing module is introduced to realize the integration of spatial and temporal sensor data. The model was trained and validated using a comprehensive database generated from large-scale fire dynamics simulations. Hyperparameter optimization, including a learning rate of 0.001 and a 5 × 5 convolution kernel size, can effectively avoid the systematic errors introduced by interpolation preprocessing, further enhancing model robustness. Validation in simplified scenarios demonstrated a mean squared error of 0.0042 m and a mean positional deviation of 0.095 m for the fire source location. Moreover, the present study assessed the model’s timeliness and reliability in full-scale cabin complex scenarios. The model maintained high performance across varying heights within cargo compartments, achieving a correlation coefficient of 0.99 and a mean absolute relative error of 1.9%. Noteworthily, reasonable location accuracy can be achieved with a minimum of three detectors, even in obstructed environments. These findings offer a robust tool for enhancing fire safety systems in aviation and other similar complex scenarios. Full article

(This article belongs to the Section Fire Risk Assessment and Safety Management in Buildings and Urban Spaces)

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16 pages, 1075 KiB

Open AccessArticle

Promoting Domestic Fire-Safety: Virtual Drills as a Training Tool for Citizens

by Pedro Ubieto-Artur, Laura Asión-Suñer and César García-Hernández

Fire 2025, 8(8), 286; https://doi.org/10.3390/fire8080286 - 22 Jul 2025

Abstract

Promoting domestic fire safety is crucial for preventing and effectively managing risky situations. This study evaluated the effectiveness of virtual environments (VEs) in fire drills to improve citizens’ knowledge and safe behavior in domestic settings. Conducted at the Citizen School for Risk Prevention (CSRP) in Zaragoza (Spain), the experiment involved 20 participants facing a simulated kitchen fire using a combination of physical and virtual extinguishing equipment. A theoretical session accompanied the drills to reinforce learning. Participants were divided into two groups: one completed the drill before and after the theoretical session, while the other completed it only afterward. Performance was assessed based on the ability to extinguish, control, or lose control of the fire. Surveys administered before, immediately after, and three months after training measured knowledge retention and behavioral changes. The results indicate a significant improvement in fire safety awareness and lasting adoption of safe practices. Participants also emerged as safety advocates. This study highlights the potential of combining theoretical instruction with immersive practical training and identifies strategies for replicating this approach in other prevention schools. Full article

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Fire, Volume 8, Issue 8 (August 2025) – 4 articles

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