Search Results (254)

Conventional wildfire assessment products emphasize regional-scale ignition likelihood and potential spread derived from fuels and weather. While useful for broad planning, they do not directly support boundary-aware, scenario-specific decision-making for localized threats to communities in the Wildland–Urban Interface (WUI). This limitation constrains the ability of fire managers to effectively prioritize mitigation efforts and response strategies for ignition events that may lead to severe local impacts. This paper introduces WUI-BTI—a scenario-based, simulation-driven boundary-threat index for the Wildland–Urban Interface that quantifies consequences conditional on an ignition under standardized meteorology, rather than estimating risk. WUI-BTI evaluates ignition locations—referred to as Fire Amplification Sites (FAS)—based on their potential to compromise the defined boundary of a community. For each ignition location, a high-resolution fire spread simulation is conducted. The resulting fire perimeter dynamics are analyzed to extract three key metrics: (1) the minimum distance of fire approach to the community boundary ($D_{min}$) for non-breaching fires; and for breaching fires, (2) the time required for the fire to reach the boundary ($T_p$), and (3) the total length of the community boundary affected by the fire ($L_c$). These raw outputs are mapped through monotone, sigmoid-based transformations to yield a single, interpretable score: breaching fires are scored by the product of an inverse-time urgency term and an extent term, whereas non-breaching fires are scored by proximity alone. The result is a continuous boundary-threat surface that ranks ignition sites by their potential to rapidly and substantially compromise a community boundary. By converting complex simulation outputs into scenario-specific, boundary-aware intelligence, WUI-BTI provides a transparent, quantitative basis for prioritizing fuel treatments, pre-positioning suppression resources, and guiding protective strategies in the WUI for fire managers, land use planners, and emergency response agencies. The framework complements regional hazard layers (e.g., severity classifications) by resolving fine-scale, consequence-focused priorities for specific communities. Full article

Developing new energy vehicles (NEVs) is a key strategy for achieving low-carbon and sustainable transportation. However, as the number of NEVs increases, traffic accidents involving these vehicles have risen sharply. To explore the characteristics of NEV accident types, and assess the occurrence of different accident types, this study proposes an accident type analysis and prediction method based on a novel Naive Bayes algorithm integrating the additive smoothing and synthetic minority over-sampling technique (AS-Naive Bayes). First, typical accident data (such as scraping, collisions, run-overs, rollovers, and battery fires/explosions) are extracted from the traffic management platform. A statistical analysis is then conducted to assess the relationships between accident types and factors including road conditions, time, vehicle status, and driver behavior. Moreover, to reduce the influence of irrelevant factors, Chi-square testing and Mutual Information are used to select features strongly associated with accident types. After that, to address the challenges of limited sample size and imbalanced distribution of accident types, this study proposes an accident type prediction method based on the AS–Naive Bayes algorithm, which integrates the Synthetic Minority Over-sampling Technique (SMOTE) and additive smoothing. Finally, five-fold cross-validation results show that the proposed method achieves a prediction accuracy of 84.8%, outperforming Support Vector Machine (SVM, 74.1%) and Long Short-Term Memory (LSTM, 79.8%), and standard Naive Bayes models, demonstrating its effectiveness in accurately identifying NEV accident types. Full article

Composite slabs have gained popularity in modern high-rise construction due to their superior load-bearing capacity and reduced self-weight. The vulnerability of the unprotected steel deck under fire conditions poses serious challenges, as the rapid reduction in steel strength and stiffness can compromise structural resistance and accelerate fire spread. This study presents a comprehensive numerical simulation to assess the fire behaviour of a novel composite slab and a new proposal for a simplified method. Three insulation techniques are investigated: a steel shield for the thinner part, a steel shield with the cavity filled with mineral wool, and a mineral wool plate applied from below. The simplified method is proposed to evaluate the fire resistance using new empirical coefficients, recalibrated within the framework of the prEN 1994-1-2 to allow for precise temperature predictions in steel components under standard fire. The numerical model, validated against experimental results, shows that the steel shield insulation extends the time to reach critical temperatures by approximately 25%. In contrast, mineral wool insulation proved to be substantially more effective by reducing temperatures in the UPPER 2 region by up to 89% compared to uninsulated slabs, after 60 min of fire exposure. This significant temperature reduction increases the load-bearing capacity during 60 min of fire exposure by 29%, also resulting in a potential reduction of approximately 22% in carbon emissions. The findings underscore and highlight the potential of these insulation systems to enhance the overall safety and resilience of composite slabs under fire, offering valuable insights for structural fire design. Full article

Sandwich panels, widely used in factory and warehouse construction, are highly susceptible to fire due to their fragile surfaces and polyurethane-insulated cores. Such structures facilitate rapid fire spread, significantly increasing the risk of extensive thermal damage. Although conventional measures, such as surface pre-wetting, are commonly utilized, their effectiveness is limited due to rapid evaporation. To address this issue, the current study evaluates the effectiveness of compressed air foam (CAF) applied as a pre-application treatment for delaying fire spread. Full-scale fire experiments were conducted to measure temperature variations across sandwich panel surfaces treated under three different conditions: untreated, water-treated, and CAF-treated. Experimental results indicated that CAF effectively formed a stable insulating barrier, maintaining temperatures well below critical thresholds, compared to untreated and water-treated panels. CAF application demonstrated superior thermal protection, reducing internal temperatures by up to 78% compared to untreated conditions and by 67.5% compared to water-treated conditions. These findings underscore the practical importance of adopting CAF pre-application as a proactive fire mitigation strategy, significantly enhancing fire safety standards in industrial and storage facilities constructed with sandwich panels. Full article

This study employed Deep Abstract Networks (DANets), independently and in combination with the Whale Optimization Algorithm (WOA), to generate high-resolution susceptibility maps for drought and wildfire hazards in the Oroqen Autonomous Banner in Inner Mongolia. Presence samples included 309 wildfire points from MODIS active fire data and 200 drought points derived from a custom Standardized Drought Condition Index. DANets-WOA models showed clear performance improvements over their solitary counterparts. For drought susceptibility, RMSE was reduced from 0.28 to 0.21, MAE from 0.17 to 0.11, and AUC improved from 85.7% to 88.9%. Wildfire susceptibility mapping also improved, with RMSE decreasing from 0.39 to 0.36, MAE from 0.32 to 0.28, and AUC increasing from 78.9% to 85.1%. Loss function plots indicated improved convergence and reduced overfitting following optimization. A pairwise z-statistic analysis revealed significant differences (p < 0.05) in susceptibility classifications between the two modeling approaches. Notably, the overlap of drought and wildfire susceptibilities within the forest–steppe transitional zone reflects a climatically and ecologically tense corridor, where moisture stress, vegetation gradients, and human land-use converge to amplify multi-hazard risk beyond the sum of individual threats. The integration of DANets with the WOA demonstrates a robust and scalable framework for dual hazard modeling. Full article

The Dazhuzhuang, Biting, and Likou Sites are located along the Hui River basin in Yongcheng, eastern Henan. These three sites are situated close to each other and all yielded Longshan Culture period (2300–1800 BCE) remains, including large quantities of pottery with similar stylistic characteristics. However, archaeological surveys did not discover kiln sites at any of the three locations. To investigate the sources of Longshan period pottery in this region, its firing technology, and whether pottery circulated between the sites, this study employed a combination of X-ray fluorescence spectroscopy (XRF), thermogravimetric analysis (TGA), and scanning electron microscopy (SEM) to conduct a comprehensive scientific analysis of pottery unearthed from Longshan Culture contexts at the Dazhuzhuang, Likou, and Biting Sites in the Huai River basin, Yongcheng, Henan Province. The results reveal significant differences among the sites in terms of raw material selection, chemical composition, and technological characteristics. Pottery from the Dazhuzhuang Site exhibits with diverse clay sources. The Likou Site is characterized by highly homogeneous compositions derived from relatively high-alumina, low-iron clays, indicating standardized production practices. In contrast, the Biting Site shows greater variability in raw materials and functional differentiation. Thermal and microstructural analyses indicate that the dense glassy phase of black pottery was achieved through reducing firing conditions. In contrast, gray pottery was manufactured with calcareous additives to produce a porous structure. Full article

This work presents an integrated experimental and machine learning study on the fire performance of sisal fiber-reinforced polyester composites treated with magnesium hydroxide as a flame retardant. A total of 43 small-scale fire resistance tests were conducted in a custom-built gas-fired furnace following ISO 834 and NCh935/2 standards. Key parameters—including fiber content, flame retardant proportion, catalyst, and accelerator—were correlated with burn time and mass loss. Linear regression revealed negligible to weak correlations, while nonlinear models (Random Forest, Support Vector Regression, and Deep Neural Network) showed improved predictive capacity. The Deep Neural Network achieved the best performance (MSE = 0.061, R² = 0.334). Experimental results confirm that magnesium hydroxide substantially increases burn time, whereas sisal fiber content alone has a minimal effect on fire resistance. This study highlights an affordable strategy for enhancing the fire safety of bio-based composites and demonstrates the potential of machine learning to optimize material formulations. Future research should expand the dataset and validate the models through standardized large-scale fire tests. However, the findings are limited to small-scale fire resistance tests under controlled laboratory conditions and should not be generalized to full-scale structural applications without further validation. Full article

The precise forecasting of renewable energy production and usage is essential for the stability, efficiency, and sustainability of contemporary power systems. This requirement is especially urgent in South Africa, a nation currently grappling with considerable energy issues, such as recurrent load shedding, outdated coal-fired power plants, and an increasing electricity demand. As the country moves towards a more renewable-focused energy portfolio, the capacity to anticipate future energy requirements is crucial for effective planning, operational stability, and grid resilience. This study introduces a hybrid approach that combines deep learning and machine learning techniques, specifically integrating long short-term memory (LSTM) neural networks with extreme gradient boosting (XGBoost) to provide more accurate and detailed forecasts of energy demand. LSTM networks are particularly effective in capturing long-term temporal dependencies in sequential data, such as patterns of energy usage. At the same time, XGBoost delivers high-performance gradient-boosted decision trees that can manage non-linear relationships and noise present in extensive datasets. The proposed hybrid LSTM-XGBoost model was trained and assessed using high-resolution data on energy consumption and weather conditions gathered from a coastal municipality in KwaZulu-Natal, South Africa, a country that exemplifies the convergence of renewable energy potential and challenges related to energy reliability. The preprocessing steps, including normalization, feature selection, and sequence modeling, were implemented to enhance the input data for both models. The performance of the model was thoroughly evaluated using standard statistical metrics, specifically the mean absolute error (MAE), the root mean squared error (RMSE), and the coefficient of determination (R²). The hybrid model achieved an MAE of merely 192.59 kWh and an R² of approximately 0.71, significantly surpassing the performance of the individual LSTM and XGBoost models. These findings highlight the enhanced predictive capabilities of the hybrid model in capturing both temporal trends and feature interactions in energy consumption behavior. Full article

To investigate the residual load-bearing capacity of composite steel truss bridge girders after vehicle fire, a 100 m simple supported composite steel truss bridge girder was selected as the research object, and a typical oil tanker fire was taken as the fire scenario. This study identifies the most critical conditions associated with an oil tanker fire and outlines the degradation pattern of the residual load-bearing capacity of composite steel truss bridge girders after a vehicle fire. It also proposes a damage classification standard and an evaluation method for the load-bearing capacity based on the structural failure path and load-displacement curve. The results indicate that the most critical scenario during a vehicle fire occurs when the fire is located on the bridge deck, particularly in the middle section of the longitudinal bridge and the outermost lane of the transverse bridge. During a vehicle fire, the top chord is the component most affected by the thermal history. Under immersion cooling conditions, the remaining load-bearing capacity of the girder decreases more significantly compared with natural cooling. After the fire, the upper chord first reaches the yield strength, causing load transfer to adjacent horizontal inclined members. The stress of the horizontal inclined rod will develop rapidly, leading to structural instability and eventual failure. Four grades of load-bearing capacity damage for composite steel truss bridge girders after vehicle fire are defined to serve as references for practical engineering applications. Full article

This study assessed the effectiveness of stress-based cognitive-behavioral training compared to standard training in firefighters, emphasizing their ability to distribute extinguishing water and cool environments evenly during enclosure fires. Experiments took place at the Zbiroh training facility with two firefighter teams (Team A with stress-based training and Team B with standard training) under realistic conditions. Using 58 thermocouples and 4 radiometers, temperature distribution and radiant heat flux were measured to evaluate water distribution efficiency and cooling performance during interventions. Team A consistently achieved temperature reductions of approximately 320 °C in the upper layers and 250–400 °C in the middle layers, maintaining stable conditions, whereas Team B only achieved partial cooling, with upper-layer temperatures remaining at 750–800 °C. Additionally, Team A recorded lower radiant heat flux densities (e.g., 20.74 kW/m² at 0°) compared to Team B (21.81 kW/m²), indicating more effective water application and adaptability. The findings confirm that stress-based training enhances firefighters’ operational readiness and their ability to distribute water effectively during interventions. This skill is essential for safer and effective management of indoor fires under extreme conditions. This study supports the inclusion of stress-based and scenario-based training in firefighter education to enhance safety and operational performance. Full article

The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper presents the design and development of an innovative self-balancing microvehicle under the H2020 LEONARDO project, which aims to address these challenges through advanced engineering and user-centric design. The vehicle combines features of monowheels and e-scooters, integrating cutting-edge technologies to enhance safety, stability, and usability. The design adheres to European regulations, including Germany’s eKFV standards, and incorporates user preferences identified through representative online surveys of 1500 PLEV users. These preferences include improved handling on uneven surfaces, enhanced signaling capabilities, and reduced instability during maneuvers. The prototype features a lightweight composite structure reinforced with carbon fibers, a high-torque motorized front wheel, and multiple speed modes tailored to different conditions, such as travel in pedestrian areas, use by novice riders, and advanced users. Braking tests demonstrate deceleration values of up to 3.5 m/s², comparable to PLEV market standards and exceeding regulatory minimums, while smooth acceleration ramps ensure rider stability and safety. Additional features, such as identification plates and weight-dependent motor control, enhance compliance with local traffic rules and prevent misuse. The vehicle’s design also addresses common safety concerns, such as curb navigation and signaling, by incorporating large-diameter wheels, increased ground clearance, and electrically operated direction indicators. Future upgrades include the addition of a second rear wheel for enhanced stability, skateboard-like rear axle modifications for improved maneuverability, and hybrid supercapacitors to minimize fire risks and extend battery life. With its focus on safety, regulatory compliance, and rider-friendly innovations, this microvehicle represents a significant advancement in promoting safe and sustainable urban mobility. Full article

The time fluctuations of forest fires occurring in Basilicata, a region situated in Southern Italy, between 2004 and 2023 were investigated using various analytical approaches. Analysis revealed a clustering of fire occurrences over time, as indicated by a significantly high coefficient of variation. This suggests that the fire sequence does not follow a Poisson distribution and instead exhibits a clustered structure, largely driven by the heightened frequency of events during the summer seasons. The analysis of monthly forest fire occurrences and total burned area indicates a significant correlation between the two. This correlation is reinforced by shared patterns, notably an annual cycle that appears to be influenced by meteorological factors, aligning with the yearly fluctuations in the region’s weather conditions typical of a Mediterranean climate. Furthermore, the relationship between the Standardized Precipitation Evapotranspiration Index (SPEI) and forest fires revealed that the accumulation period of the SPEI corresponds to the cycle length of the fires: longer cycles in fire occurrences align with higher accumulation periods in SPEI data. Full article

The increasing adoption of Light Electric Vehicles (LEVs) in urban areas, driven by the micromobility wave, raises significant safety concerns, particularly regarding battery fire incidents. This research investigates the electromechanical performance of aged 18650 lithium-ion batteries (LIBs) from LEVs under mechanical impact conditions. For this study, a battery module from a used e-scooter was disassembled, and its constituent cells were reconfigured into compact modules for testing. To characterize their initial condition, the cells underwent cycling tests to evaluate their state of health (SOH). Although a slight majority of the cells retained an SOH greater than 80%, a notable increase in their internal resistance (IR) was also observed, indicating degradation due to aging. The mechanical impact tests were conducted in adherence to the UL 2271:2018 standard, employing a semi-sinusoidal acceleration pulse. During these tests, linear kinematics were analyzed using videogrammetry, while key electrical and thermal parameters were monitored. Additionally, strain gauges were installed on the central cells to measure stress and deformation. The results from the mechanical shock tests revealed characteristic acceleration and velocity patterns. These findings clarify the electromechanical behavior of aged LIBs under impact, providing critical data to enhance the safety and reliability of these vehicles. Full article

The oil and gas industry is subject to significant fire hazards due to the flammability of hydrocarbons and the extreme conditions of operational facilities. Intumescent coatings (ICs) serve as a crucial passive fire protection strategy, forming an insulating char layer when exposed to heat, thereby reducing heat transfer and delaying structural failure. This review article provides an overview of recent developments in the effectiveness of ICs in mitigating fire risks, enhancing structural resilience, and reducing environmental impacts within the oil and gas industry. The literature surveyed shows that analytical techniques, such as thermogravimetric analysis, scanning electron microscopy, and large-scale fire testing, have been used to evaluate the thermal insulation performances of the coatings. The results indicate significant temperature reductions on protected steel surfaces that extend critical failure times under hydrocarbon fire conditions. Recent advancements in nano-enhanced and bio-derived ICs have also improved thermal stability and mechanical durability. Furthermore, numerical modeling based on heat transfer, mass conservation, and kinetic equations aids in optimizing formulations for real-world applications. Nevertheless, challenges remain in terms of standardizing modeling frameworks and enhancing the environmental sustainability of ICs. This review highlights the progress made and the opportunities for continuous advances and innovation in IC technologies to meet the ever-evolving challenges and complexities in oil and gas industry operations. Consequently, the need to enhance fire protection by utilizing a combination of tools improves predictive modeling and supports regulatory compliance in high-risk industrial environments. Full article

Hydrological processes in river systems are changing due to climate variability and human activities, making it crucial to understand and quantify these changes for effective water resource management. This study examines long-term trends in hydroclimate variables (1990–2022) and land use/land cover (LULC) changes (1988, 2002, and 2022) within the Continental Reach of the Gambia River Basin (CGRB). Trend analyses of the Standardized Precipitation-Evapotranspiration Index (SPEI) at 12-month and 24-month scales, along with river discharge at the Simenti station, reveal a shift from dry conditions to wetter phases post-2008, marked by significant increases in rainfall and discharge variability. LULC analysis revealed significant transformations in the basin. LULC analysis highlights significant transformations within the basin. Forest and savanna areas decreased by 20.57 and 4.48%, respectively, between 1988 and 2002, largely due to human activities such as agricultural expansion and deforestation for charcoal production. Post-2002, forest cover recovered from 32.36 to 36.27%, coinciding with the wetter conditions after 2008, suggesting that climatic shifts promoted vegetation regrowth. Spatial analysis further highlights an increase in bowe and steppe areas, especially in the north, indicating land degradation linked to human land use practices. Bowe areas, marked by impermeable laterite outcrops, and steppe areas with sparse herbaceous cover result from overgrazing and soil degradation, exacerbated by the region’s drier phases. A notable decrease in burned areas from 2.03 to 0.23% suggests improvements in fire management practices, reducing fire frequency, which is also supported by wetter conditions post-2008. Agricultural land and bare soils expanded by 14%, from 2.77 to 3.07%, primarily in the northern and central regions, likely driven by both population pressures and climatic shifts. Correlations between precipitation and land cover changes indicate that wetter conditions facilitated forest regrowth, while drier conditions exacerbated land degradation, with human activities such as deforestation and agricultural expansion potentially amplifying the impact of climatic shifts. These results demonstrate that while climatic shifts played a role in driving vegetation recovery, human activities were key in shaping land use patterns, impacting both precipitation and stream discharge, particularly due to agricultural practices and land degradation. Full article