Fire

Reinforced concrete (RC) slabs with internal voids are increasingly used to improve material efficiency; however, their residual structural performance after fire exposure remains insufficiently understood. This study presents a numerical investigation of RC slabs with different void geometries using a three-dimensional nonlinear Finite Element (FE) model. A sequential thermal–structural approach was adopted, in which fire exposure was simulated through transient thermal analysis, and the resulting spatial distribution of maximum temperatures was used to assign residual material properties to each FE based on its local peak temperature, followed by structural analysis under ambient conditions. A parametric study was conducted on seven slab configurations, including two solid slabs and five voided slabs with spherical, elliptical, ellipsoidal, capsule, and biaxial capsule geometries. To ensure a consistent evaluation, two reference solid slabs were considered: a 230 mm thick slab to enable comparison under identical geometric conditions, and a 160 mm thick slab representing equivalent concrete volume to assess material efficiency. Fire exposure was applied according to the ISO 834 standard fire curve for durations of 30, 60, and 90 min. The results indicate that voided slabs exhibit higher deflections than the solid slab of identical thickness due to reduced stiffness, while achieving comparable performance relative to the solid slab with equivalent concrete volume. These findings highlight the trade-off between structural stiffness and material efficiency under increasing fire exposure time. Full article

Wildfire prediction using machine learning (ML) and deep learning (DL) has expanded rapidly, yet synthesis regarding algorithmic configurations, data practices, and transparency remains limited. This systematic review characterizes ML/DL applications in wildfire prediction (2020–2025) using a PRISMA-EcoEvo framework across 341 peer-reviewed studies, with detailed analysis of 110 articles from 2024. Publication output increased steadily, concentrated geographically in China and the United States. Methodologically, ensemble tree-based methods (26.7%) and deep learning architectures (59.4%) coexist, reflecting adaptation to diverse data modalities. Input data are dominated by vegetation/fuel characteristics (44.7%) and historical fire labels (41.2%), while socioeconomic variables remain marginal (1.2%). Evaluation practices distinguish classification and regression tasks, yet metric heterogeneity constrains cross-study comparability. Critically, only 7.7% of studies provided publicly accessible code, with a significant association between algorithm family and code availability (χ² = 78, p = 0.0012). Collectively, wildfire ML/DL research demonstrates technical advancement but remains geographically concentrated and constrained by limited transparency. Strengthening reporting standards, metric-task alignment, dataset documentation, and open-code practices is essential to translate computational innovation into globally robust, reproducible wildfire decision-support systems. Full article

Aiming at the inherent bottlenecks of traditional smoke detection technologies in high and large-volume building scenarios, this paper conducts research on an early fire smoke detection method for high and large-volume spaces based on Light Detection and Ranging (LiDAR). A special experimental platform was independently designed to obtain the physical characteristics of smoke particles from standard smoldering fires. Combined with the optical scattering and reflection interaction mechanism between laser and particulate matter, the theoretical feasibility of LiDAR for smoke detection was systematically verified. Smoke irradiation experiments were carried out in the full detection distance, and the LiDAR point cloud characterization characteristics of smoldering smoke were clarified. A special smoke detection algorithm based on point cloud features was designed, a LiDAR smoke detection system was built, and multi-condition comparative experiments with traditional photoelectric smoke detection methods were carried out in a full-scale laboratory. The experimental results show that the LiDAR-based smoke detection method proposed in this paper has significant advantages over traditional detection methods in terms of alarm response speed, detection coverage, and height adaptability. This research provides a brand-new technical path and reference for the theoretical research and engineering application of early fire warning technology for high and large-volume buildings. Full article

Accurate and efficient modeling of laminar premixed flames is essential for chemical mechanism validation and parametric studies in combustion science. For this purpose, CombF was developed—a semi-analytical computational framework for one-dimensional (1D) laminar premixed flames—offering flexible control over nodal distributions and optional incorporation of experimental temperature data. Unlike conventional fully coupled solvers, CombF explicitly separates the initialization and solution stages, enabling structured control over intermediate structure and temperature constraints while preserving physical consistency. The methodology employs linear interpolation between pre- and post-reaction equilibrium states, adaptive grid refinement, and finite-difference solutions of species and energy conservation equations, with radiation heat transfer optionally included. CombF was validated for ethylene–air premixed flames by comparison with experimental data under varying equivalence ratios and inlet velocities using the YARC-AF kinetic mechanism, and for methane–air premixed flames by additional benchmark comparisons with Cantera, employing the DRM22 mechanism. CombF predictions were further validated against methane and propane–air flames under varying inlet compositions and velocities using the Diego mechanism and evaluated using the curve matching (CM) score, L₂ norms, and phase shift alignment via a nonparametric bootstrap approach. The results demonstrate strong agreement for major species (CO₂, H₂O), while intermediate species (CO, CH₂O) show higher sensitivity to temperature profile choice and nodal resolution, providing a more discriminating assessment of model fidelity. Incorporating experimental temperature fields substantially improves species distribution accuracy and structural alignment. Thus, CombF provides a reliable, flexible, and experimentally adaptive framework that is capable of accurately capturing flame structures, offering a practical tool for preliminary analyses, parametric exploration, and instructional applications in combustion research. Full article

Near-surface wind field simulation in complex mountainous terrain is essential for predicting wildfire behavior and supporting fire risk management. WindNinja, a widely used diagnostic wind downscaling model, is strongly dependent on its initial input data; however, systematic evaluations of its input sensitivity and simulation accuracy remain limited. In this study, a representative canyon area was selected as the study site. WindNinja was driven by three types of input data: local meteorological station observations, national meteorological station observations, and ERA5-Land reanalysis data. Two indices—the Wind Forcing Intensity (WFI) index and the Thermal Forcing Intensity (TFI) index—were constructed to classify weather-forcing scenarios and evaluate simulation accuracy under different conditions. The results show that differences in the statistical characteristics of the initial wind sources produce pronounced sensitivity in WindNinja simulations. Simulations driven by local meteorological observations generally overestimate wind speed, whereas ERA5-Land-driven simulations systematically underestimate wind speed, with national-station results falling between these two cases. Simulation accuracy varies with terrain position: wind direction errors dominate in valleys, whereas wind speed errors dominate on ridges and hilltops. Weather background conditions significantly influence simulation accuracy. Wind forcing intensity dominates the magnitude and dispersion of simulation errors, while strong thermal forcing leads to an overall decline in simulation accuracy and stability. These findings highlight the sensitivity of WindNinja to initial wind sources and weather background conditions in complex terrain and provide guidance for its application and uncertainty control in wildfire behavior modeling. Full article

Wildland fire scientists have made substantial advances in measuring fire behavior, but properly collecting data is often beyond the capacity of prescribed fire managers and by definition all but impossible for wildfire events. While a method for the immediate assessment of burn severity has been developed around multispectral imagery from space-based Earth observation systems, there has been little comparison of these post hoc metrics to actual fire behavior. Meanwhile, the application of research results from experimental prescribed burns to rangeland affected by wildfire can be impeded by a lack of understanding of how immediate burn severity differs between wildfires and prescribed burns, especially in rangelands. Overall, much of what is known about wildland fire behavior, severity, and effects comes from forests, whereas rangelands are characterized by having lower fuel loads comprised of fine vegetation that promotes high rates of spread and brief residence time. This paper provides rangeland-specific information on the relationships between direct field-based fire behavior measurements and a space-based index of burn severity (differenced Normalized Burn Ratio, ΔNBR, from Sentinel-2 imagery), and uses those data to compare burn severity across 54 prescribed burns in North Dakota, USA, and 28 nearby wildfires in the US Northern Great Plains. In prescribed burns, remotely sensed burn severity increased with rate of spread and flame temperature 15 cm above the ground, but had no statistically significant relationship with soil surface temperature. In the semi-arid western zone of the Northern Great Plains, wildfires and prescribed burns had similar, low–moderate severity; wildfires in the eastern zone tended to be of moderately high severity and thus greater than the low severity of the experimental prescribed burns. By describing meaningful gradients in surface fire behavior in rangelands with ΔNBR, even those without the capacity to measure fire behavior in the field can monitor prescribed fire effectiveness and incorporate burn severity in adaptive management plans. Understanding the relationship between burn severity across wildfires and prescribed burns is a critical step in applying knowledge gained from research on prescribed fires to areas impacted by wildfire. Resistance to prescribed burning might be overcome by increasing livestock managers’ experience with post-fire forage resources through grazing areas burned in unintentional wildfires, but current practice and policy discourage or outright prevent ranchers from doing so. Future research ought to connect burn severity with ecosystem recovery metrics to ensure post-fire grazing does not impair rangeland sustainability. Full article

Wildfires may significantly alter the mineralogical and microstructural characteristics of geological materials, leading to increased susceptibility to landslides, debris flows, and other related hazards. These processes may involve considerable post-fire hydrological changes that affect the infiltration rate and the surface runoff in the burned soils. In the present study, a laboratory experimental investigation is carried out focusing on the water infiltration in burned soils which were produced in a muffle furnace at accurately controlled temperatures within 400 °C∼800 °C. The original and burned soils were first subjected to a number of geotechnical tests, including grain size distribution, consistency, and hydraulic conductivity. Subsequently, their water infiltration rates were measured in a laboratory setup. Finally, numerical simulations are performed to assess the infiltration process based on the Green–Ampt model. The experimental results reveal significant differences in the hydrological behavior between burned and unburned soils. Overall, burned soils experienced quicker ponding and slower infiltration. However, as the burning temperature increased from moderate to high, the infiltration rate also rose considerably, along with delayed ponding time. This trend may be related to the microstructural change in the grain size distribution explored experimentally in the present study. The numerical results are highly consistent with the experimental data. The hydraulic conductivity is identified as the predominant parameter in the infiltration process examined and simulated in the present study. Its evolution with varied burning temperatures can also be traced to the fire-induced alteration in the grain size distribution, and primarily accounts for the differences in the infiltration of different soil specimens. The present study demonstrates the potential of laboratory experiments complemented with a quantitative modeling approach in improving our understanding of soil’s post-fire hydrological responses. Full article

In a tank farm, even if the separation distance meets the codes and standards, a pool fire in one tank may spread quickly to another tank. Most destructive and uncontrollable fire accidents are induced with multiple pool fires. In current work, the thermal buckling behaviors of a fixed-roof tank subjected to one (two) neighboring pool fire(s) (burning tanks) are numerically studied. The effects of the number of the pool fires, the separation distance between two pool fires, and the distance between the adjacent tank and pool fires are analyzed. The results indicate that the thermal buckling zone of the target tank subjected to two pool fires is larger than that subjected to one pool fire, and the maximum displacement for two pool fires is almost equal to that for one pool fire. The target tank subjected to one pool fire loses stability and reaches a new stable state faster than that subjected to two pool fires. The thermal buckling zone expands as the distance between the two pool fires increases but decreases with increasing separation distance between the pool fire and the target tank. The findings provide useful guidance for the structural optimization of steel storage tanks against pool fire exposure and offer theoretical support for emergency response and fire rescue in tank farms. Full article

Accurate smoke forecasting during wildfires is essential for hazard assessment and public health protection. Current operational models have limitations in representing dynamic fire-atmosphere interactions. This study aimed to assess the performance of the fire-atmosphere coupled version of the Warn-on-Forecast System (WoFS) and compare it with the classic WoFS in simulating wildfire smoke distribution and structure. Two Oklahoma wildfire events were simulated, and model outputs were compared against radar reflectivity observations for plume-top height, horizontal dispersion, and vertical structure. Both models showed comparable agreement with observations. WoFS-Smoke performed similarly or better in the early forecast period (0–1 h) due to direct smoke injection, whereas WRF-SFIRE, using a WoFS environment, required ~1 h spin-up before producing more realistic, continuous plume structures through fire-atmosphere coupling. SFIRE tended to overestimate plume height in one case and underestimate it in another. Coupling WoFS to SFIRE generally produced more realistic forecast smoke plume characteristics resulting from the dynamical coupling between the forecast environment and wildfire properties. The combination of WoFS and WRF-SFIRE opens up new possibilities in short-term wildfire smoke forecasting, setting the foundation for future operational models. Full article

Passive Fourier transform infrared (FTIR) spectral imaging technology is easily affected by complex background radiation for leakage monitoring at natural gas stations, leading to low gas identification sensitivity, poor detection accuracy and a high false alarm rate. To address these issues, the spectral characteristics of typical background interference sources and their impact mechanisms were first analyzed in this work. Subsequently, a targeted background denoising method was developed and then on-site gas release experiments were conducted in a typical natural gas station. The results demonstrated that the proposed background denoising method can effectively suppress complex environmental background interference and reduce the false alarm rate. This study provides a solution for enhancing the reliability and practicality of passive FTIR spectral imaging technology in remote gas leakage monitoring at industrial sites. Full article

Despite the well-known strong influence of spatial resolution on the quality of burned area mapping and the need for timely environmental information, global wildfire monitoring services are commonly based on coarse spatial resolution (300–500 m) reflectance imagery and deliver products months or years after the present date. The paper presents, for the first time, an algorithm that provides highly accurate near-real-time medium spatial resolution burned area, from 20 m Sentinel-2 imagery. The paper exploits a pioneering sensor-independent potential of a mapping method, based on land surface reflectance modelling and machine learning, originally optimised for Sentinel-3 imagery. The mapping method uses predictions of time series of burned area from a neural network, which are combined with the spatio-temporal density of active fire detections. The mapping method was calibrated and validated using reference datasets for the years 2020 and 2019, respectively. The novelty of this method lies in its high accuracy and multi-latency flexibility: it achieves a Dice coefficient (DC) of 82.7% with zero-day latency, already surpassing the 81.8% accuracy of current state-of-the-art non-time critical methods. As reflectance data availability increases, accuracy scales to DC 84.7% and 85.4% with 5 and 10 days of latency, respectively, and to DC 87.2% for monthly composites with 45 days of latency. Full article

Immersive entertainment venues use spatial complexity to enhance visitor experience, but these design features may impair emergency evacuation, particularly when lighting fails. We conducted a 2 × 2 factorial experiment with 264 participants to quantify how spatial complexity and lighting conditions interact to affect evacuation performance. Ultra-wideband positioning provided centimeter-level tracking. Results showed very large main effects for spatial complexity (${\eta }_p^2$ = 0.976) and lighting (${\eta }_p^2$ = 0.863), but critically, a significant interaction (${\eta }_p^2$ = 0.799) revealed asymmetric patterns: darkness barely affected simple spaces (6.5% increase) but severely impaired complex spaces (36.9% increase), with a nine-fold amplification. The worst-case scenario (high complexity + darkness) increased evacuation time by 115% compared to optimal conditions. Findings demonstrate that spatial complexity and lighting combine synergistically, creating multiplicative rather than additive risk, with the worst-case combination increasing evacuation time by 115% relative to optimal conditions. Findings support prioritizing spatial simplification and emergency lighting redundancy in the design of complex immersive venues. Full article

Obstacles can significantly affect the thermal radiation distribution of oil storage tank fires; however, this issue has received relatively limited attention in previous studies. Taking aviation kerosene fires as an example, this study employed a cylindrical flame radiation model combined with the Monte Carlo method to investigate the variation in the radiative flux incident on the target and the flame-target view factor under different obstacle widths (W), heights (H) and target distances (d). The results indicate that obstacles block the flame radiation path, thereby reducing the radiative flux in the region behind the obstacle compared with the unobstructed condition. The view factor first decreases with increasing W and then approaches a stable value. The critical width (W_cr) is independent of H but increases with d. A similar relationship is observed between H and the critical height (H_cr). Based on geometric analysis, analytical expressions for W_cr and H_cr were derived. In addition, a predictive model for the view factor shielding ratio (φ) was established using three dimensionless geometric parameters, achieving a coefficient of determination of R² = 0.976, which demonstrates good predictive accuracy. These findings provide theoretical guidance for fire risk assessment in tank farm areas. Full article

This study establishes an experimental platform consisting of an adjustable inclined surface and a cross-slope wind system. Turbulent diffusion flames are investigated by examining the variation characteristics of flame morphology under slope angles of 10–40°, cross-slope wind velocities of 0.8–2.0 m/s, and heat release rates of 15.38–61.50 kW. The results show that variations in slope angle change the components of buoyancy in the normal and tangential directions. The normal component influences the lifting of the flame perpendicularly to the slope, while the tangential component, together with differences in air entrainment on both sides of the flame, promotes flame inclination and spreading along the slope surface. The cross-slope wind enhances the horizontal stretching and attachment tendency of the flame through inertial shear, while simultaneously suppressing flame height and its development along the slope. The coupled effects of these factors cause the flame morphology to gradually transition from a nearly vertical state to an attached state. Based on dimensionless analysis, empirical correlations of flame morphology parameters are established by introducing the cross-slope wind Froude number, dimensionless heat release rate, the density ratio of propane to air, and a slope function. Within the experimental range of this study, the data under various conditions show good collapse and correlation under the selected dimensionless parameters. Full article

Polyurethane (PU) is a highly versatile class of polymer utilised in many industries, including the aerospace sector. In conjunction with its superior mechanical properties, chemical resistance, and durability, it can be highly flammable depending on its form. This poses a risk aboard aircraft, which contain numerous fire hazards and cramped cabin spaces, proving an obstacle for the evacuation of passengers in an emergency. Flame-retardant additives have proven to enhance the thermal properties of polyurethane, but their toxicity and tendency to degrade mechanical performance make them unappealing. This review addresses three main topics: (1) the basic synthesis and structure of PU and modification through additives; (2) types of PU, their properties, and applications in the aerospace industry; and (3) evaluation methodologies for characterising PU performance, studying mechanical properties and thermal degradation. Several key challenges remain, including understanding the long-term durability of modified PU, optimising between fire performance and mechanical properties, improving the sustainability of PU throughout its lifetime, and validating numerical simulation as a viable testing method. This review aims to guide future research on modified PU technology to achieve safer, high-performing, and sustainable solutions for the aerospace industry and beyond. Full article

Wildfire smoke contributes substantially to episodic PM_2.5 exposure, yet outdoor measurements may not represent indoor conditions. We analyzed indoor PurpleAir sensors and nearby outdoor monitors from U.S. residences (2019–2024) to estimate smoke-day changes in the outdoor–indoor PM_2.5 difference and characterize heterogeneity across regions. After data quality control and the application of completeness criteria, 509 monitor pairs contributed 250,873 monitor-days. Smoke days were assigned using the NOAA Hazard Mapping System smoke-plume polygons. Pair-specific time-series models estimated smoke-day changes in the outdoor–indoor PM_2.5 difference, which were pooled using random-effects meta-analysis; heterogeneity was summarized by clustering indoor and outdoor smoke–non-smoke contrasts. In the unadjusted summary, the mean outdoor PM_2.5 was 8.61 vs. 5.63 µg/m³ on smoke vs. non-smoke days and the mean indoor PM_2.5 was 6.33 vs. 5.09 µg/m³, reflecting an increase in the mean outdoor–indoor difference from 0.54 to 2.27 µg/m³ (p < 0.001). The pooled smoke-day effect on the outdoor–indoor difference was 0.88 µg/m³ (95% CI: 0.80, 0.96). Clustering identified four distinct response patterns, most commonly outdoor increases exceeding indoor increases, with smaller subsets showing extreme outdoor amplification or net indoor reductions under modest outdoor increases. These results indicate that indoor protection during smoke episodes is common but variable and support exposure characterization beyond outdoor concentrations alone. Full article

This study proposes a sensor-free approach for indoor fire detection that leverages existing Wi-Fi infrastructure as a passive sensing modality. By extracting Channel State Information (CSI) from prevalent 802.11n Wi-Fi signals and applying an unsupervised deep anomaly detection model, the approach conceptualizes the wireless environment as a sensorless detection field capable of identifying combustion-induced perturbations without requiring any physical sensors. CSI data were collected in both normal and flame-induced states under three combustion conditions (gasoline, wood, plastic), each introducing unique signal perturbations. These data, which exhibit diverse signal perturbations, were used as input to four unsupervised deep anomaly detection architectures: a variational autoencoder (VAE), a 1D convolutional autoencoder (CNN-AE), a long short-term memory autoencoder (LSTM-AE), and a hybrid CNN-LSTM autoencoder. Each architecture was trained exclusively on baseline data to learn compact latent representations of normal signal patterns. Among the evaluated architectures, CNN-AE achieved perfect detection across all scenarios, showing high responsiveness to signal disruptions. LSTM-AE tracks prolonged combustion but struggles with fast-onset anomalies. VAE maintains low error during baseline but misses sharp deviations. These findings validate that Wi-Fi CSI encodes latent combustion features. The method requires no additional sensors and operates on existing signals, enabling scalable smart building integration via lightweight software updates. Full article

In view of the practical engineering demand and performance optimization of compressed air-driven fire-extinguishing projectile launchers, a two-dimensional axisymmetric compressible flow numerical model is established based on ANSYS Fluent 2023. Numerical verification is conducted by comparing with classical zero-dimensional theoretical results and reference data from the published literature to guarantee simulation accuracy. Combined with the internal ballistic motion characteristics, the present study systematically investigates the effects of initial pressure, flow passage structure, loading position and projectile mass on launch dynamic behavior and the energy utilization mechanism. The results reveal that the initial high-pressure chamber pressure dominates the total energy output of the system. Appropriately increasing the valve gap and nozzle diameter can improve flow characteristics and energy transfer efficiency. Adjusting the loading position and barrel length effectively balances the internal ballistic response, while larger projectile mass brings higher inertial resistance and obvious efficiency attenuation. This work clarifies the quantitative influence of key structural and operating parameters, and provides theoretical support and engineering reference for the design, parameter matching and performance improvement of similar fire-extinguishing launching equipment. Full article

Prescribed fires can reduce hazardous fuel loads, but planning remains challenging in landscapes with complex terrain, mixed vegetation, and nearby infrastructure. Predicting and controlling where a prescribed fire may breach its containment lines can be carried out by integrating fire-behavior simulations with practical treatment planning. This paper presents a two-stage framework using QUIC-Fire to identify high-risk escape zones and allocate containment treatments under cost and resource constraints. Stage 1 identifies high-risk boundary segments and assigns adjacent zones to fuel removal or moisture treatment to limit simulated fire spread beyond the control line. Stage 2 refines these assignments by incorporating treatment costs and penalty values near infrastructure to evaluate resource-constrained alternatives. Applied to a 14.2-ha prescribed-fire unit in Mount Laguna, California, the optimized Stage 2 configuration maintained containment under the simulated conditions while reducing total implementation cost from USD 97,319 to USD 95,266 (approximately 2.1%) and reducing fire-engine demand from six to three. These results illustrate how cost-aware treatment reallocation can improve resource efficiency for prescribed-fire performance. Full article