Firing Time Research Articles

Post-earthquake fire performance of partially encased composite steel and concrete columns

Considering the potential severity of losses caused by post-earthquake fires compared to earthquakes alone, this study employed a three-dimensional Finite Element (FE) model established in ABAQUS to investigate the response of partially encased composite (PEC) steel and concrete columns under post-earthquake fire, serving as preliminary analysis for subsequent experimental studies. The validity of the FE model was verified using results from existing seismic and fire loading tests. In simulating the hysteresis behavior of PEC columns, this study evaluated the impacts of concrete discrete cracking and mechanical models on the simulation outcomes, thereby refining the modeling technique. A typical PEC column was selected as the focus of this investigation. Sequential earthquake-fire simulations were conducted using a data transfer technique to assess the fire resistance performance of different seismic damaged columns with different axial compression load ratios. The results indicate that the behavior of damaged PEC columns evolves based on initial damage, and the failure exhibits overall bending accompanied by local buckling of the steel flange. As the axial load ratio increases, the fire resistance time of seismic-damaged PEC columns significantly decreases. When the axial compression ratio is increased from 0.2 to 0.5, the fire resistance time of columns with damage factor D within 0.6 decreases by 59.07% on average. This sensitivity to axial pressure is particularly pronounced when earthquake damage is severe. Moreover, it finds that the lateral drift ratio under seismic action should be controlled within 2% to ensure that the post-earthquake fire resistance will not be significantly reduced.

Predictive Understanding of Links Between Vegetation and Soil Burn Severities Using Physics‐Informed Machine Learning

AbstractBurn severity is fundamental to post‐fire impact assessment and emergency response. Vegetation Burn Severity (VBS) can be derived from satellite observations. However, Soil Burn Severity (SBS) assessment—critical for mitigating hydrologic and geologic hazards—requires costly and laborious field recalibration of VBS maps. Here, we develop a physics‐informed Machine Learning model capable of accurately estimating SBS while revealing the intricate relationships between soil and vegetation burn severities. Our SBS classification model uses VBS, as well as climatological, meteorological, ecological, geological, and topographical wildfire covariates. This model demonstrated an overall accuracy of 89% for out‐of‐sample test data. The model exhibited scalability with additional data, and was able to extract universal functional relationships between vegetation and soil burn severities across the western US. VBS had the largest control on SBS, followed by weather (e.g., wind, fire danger, temperature), climate (e.g., annual precipitation), topography (e.g., elevation), and soil characteristics (e.g., soil organic carbon content). The relative control of processes on SBS changes across regions. Our model revealed nuanced relationships between VBS and SBS; for example, a similar VBS with lower wind speeds—that is, higher fire residence time—translates to a higher SBS. This transferrable model develops reliable and timely SBS maps using satellite and publicly accessible data, providing science‐based insights for managers and diverse stakeholders.

Delay learning based on temporal coding in Spiking Neural Networks

Spiking Neural Networks (SNNs) hold great potential for mimicking the brain’s efficient processing of information. Although biological evidence suggests that precise spike timing is crucial for effective information encoding, contemporary SNN research mainly concentrates on adjusting connection weights. In this work, we introduce Delay Learning based on Temporal Coding (DLTC), an innovative approach that integrates delay learning with a temporal coding strategy to optimize spike timing in SNNs. DLTC utilizes a learnable delay shift, which assigns varying levels of importance to different informational elements. This is complemented by an adjustable threshold that regulates firing times, allowing for earlier or later neuron activation as needed. We have tested DLTC’s effectiveness in various contexts, including vision and auditory classification tasks, where it consistently outperformed traditional weight-only SNNs. The results indicate that DLTC achieves remarkable improvements in accuracy and computational efficiency, marking a step forward in advancing SNNs towards real-world applications. Our codes are accessible at https://github.com/sunpengfei1122/DLTC.

Tuning stability, rheology, and fire-extinguishing performance of advanced firefighting foam material by inorganic nanoparticle flame retardants

HypothesisNanoparticle-stabilized foams are extremely stable, and flame retardant inorganic nanoparticles should be able to add sealing capacity of firefighting foams on flammable liquid fuels, and hence enhance fire extinguishment performance on liquid fuel fire. In practice, how do flame retardant nanoparticles resist the destructive effect of oil molecules on foam and tune foam properties? ExperimentsWe have prepared a nanoparticle-enhanced foam comprising of hydrocarbon surfactant, short-chain fluorocarbon surfactant, and nanoparticles. The interactions among nanoparticles and surfactant molecules were characterized by using dynamic surface tension and conductivity. Stability, rheology, and oil resistivity on liquid fuel of the nanoparticle-enhanced foam were evaluated systematically. Fire suppression effectiveness of the foams was verified based on a standard experiment. FindingsFoam stability and oil resistivity were enhanced due to self-assembled network structures formed by jammed aggregates composed by nanoparticles and surfactants in Plateau borders and bubble films, providing structural recoverability and enhanced viscoelasticity within foam. Foams containing nano-SiO2, nano-CaCO3, nano-Al(OH)3, and nano-Mg(OH)2 show difference in fire extinguishment due to different ability to enhance foam properties. Foam containing nano-Al(OH)3 shows the strongest adaptation and could shorten fire extinguishing time by 2 times and prolong burn-back time by 2.3 times compared with commercial product.

Paired competing neurons improving STDP supervised local learning in Spiking Neural Networks.

Direct training of Spiking Neural Networks (SNNs) on neuromorphic hardware has the potential to significantly reduce the energy consumption of artificial neural network training. SNNs trained with Spike Timing-Dependent Plasticity (STDP) benefit from gradient-free and unsupervised local learning, which can be easily implemented on ultra-low-power neuromorphic hardware. However, classification tasks cannot be performed solely with unsupervised STDP. In this paper, we propose Stabilized Supervised STDP (S2-STDP), a supervised STDP learning rule to train the classification layer of an SNN equipped with unsupervised STDP for feature extraction. S2-STDP integrates error-modulated weight updates that align neuron spikes with desired timestamps derived from the average firing time within the layer. Then, we introduce a training architecture called Paired Competing Neurons (PCN) to further enhance the learning capabilities of our classification layer trained with S2-STDP. PCN associates each class with paired neurons and encourages neuron specialization toward target or non-target samples through intra-class competition. We evaluate our methods on image recognition datasets, including MNIST, Fashion-MNIST, and CIFAR-10. Results show that our methods outperform state-of-the-art supervised STDP learning rules, for comparable architectures and numbers of neurons. Further analysis demonstrates that the use of PCN enhances the performance of S2-STDP, regardless of the hyperparameter set and without introducing any additional hyperparameters.

Sensitivity of Fire Indicators on Forest Inventory Plots Is Affected by Fire Severity and Time since Burning

Forest inventory data are useful for determining forest stand structure, growth, and change. Among the information collected on forest inventory plots by the USDA Forest Service Forest Inventory and Analysis Program, attributes characterizing various types of disturbance provide researchers a means of selecting plots specifically affected by disturbances, such as fire. We determine the performance of three of these attributes as indicators of recent fires on forest inventory plots of the United States by comparing them to independent records of wildland fire occurrence. The indicators are plot-level observations of fire effects on (1) general site appearance, (2) tree mortality, and (3) damage to live trees. Independent spatial layers of wildland fire perimeters provide an approach to test indicator performance and identify characteristics of fires that may affect detection. The sensitivities of indicators are generally higher in the West relative to the East. Detection rates exceed 90 percent for the Pacific Coast forests but seldom reach 80 percent in the East. Among the individual indicators, site appearance has higher identification rates than tree indicators for fires in the Pacific Coast, Great Plains, North, and South regions. Tree mortality is the most important single indicator for identifying Rocky Mountain fires. Tree damage is more important than tree mortality in the South; otherwise, the tree damage indicator is of relatively lower importance, particularly where high-severity fires are common, and tree survival is low. The rate of detection by the indicators is affected by the severity of the fire or the recency of the fire. The joint effect of severity and recency influence all three indicators for the Pacific Coast and Rocky Mountain fires, as well as the site appearance indicator in the South. Only a small proportion of fires are clearly missed by all three of the indicators.

Firing pattern manipulation of neuronal networks by deep unfolding‐based model predictive control

AbstractThe complexity of neuronal networks, characterized by interconnected neurons, presents significant challenges in control due to their nonlinear and intricate behaviour. This paper introduces a novel method designed to generate control inputs for neuronal networks to regulate the firing patterns of modules within the network. This methodology is built upon temporal deep unfolding‐based model predictive control, a technique rooted in the deep unfolding method commonly used in wireless signal processing. To address the unique dynamics of neurons, such as zero gradients in firing times, the method employs approximations of input currents using a sigmoid function during its development. The effectiveness of this approach is validated through extensive numerical simulations. Furthermore, control experiments were conducted by reducing the number of input neurons to identify critical features for control. Various selection techniques were utilized to pinpoint key input neurons. These experiments shed light on the importance of specific input neurons in controlling module firing within neuronal networks. Thus, this study presents a tailored methodology for managing networked neurons, extends temporal deep unfolding‐based model predictive control to nonlinear systems with reset dynamics, and demonstrates its ability to achieve desired firing patterns in neuronal networks.

Ultrafast Response and Threshold Adjustable Intelligent Thermoelectric Systems for Next-Generation Self-Powered Remote IoT Fire Warning

HighlightsThe flexible single-walled carbon nanotube/titanium carbide composite films exhibit excellent thermoelectric (TE), high-temperature stable and flame-retardant properties.The assembled TE device achieves an ultrafast fire warning response time of ~ 0.1 s with a threshold voltage of 1 mV.The fire warning device demonstrates exceptional repeatability and long-term stability.The designed intelligent system is promising for next-generation self-powered remote IoT fire warning applications.

Experimental and analytical study on post-fire residual flexural behavior of corroded reinforced concrete beams after various cooling methods

To study the flexural behavior of corroded reinforced concrete (RC) beams after various cooling methods, a series of tests were carried out on twenty-four RC beams. The study delved into the effects of corrosion degree (5 %, 10 %, and 15 %), fire exposure time (60 min, 90 min, and 120 min), and cooling methods (natural cooling and water cooling) on the flexural performance of the beams. The results indicated that during the cooling phase, beams subjected to water-sprayed cooling exhibited a faster cooling rate and more pronounced surface cracks in comparison to beams undergoing natural cooling. All beam specimens showed flexural failure mode, but it should be noted that water-spraying cooling may induce brittle failure in beams post-fire. The descending segment of the load-deflection curve for failed specimens with over-reinforcement was steep, indicative of poor ductility. The peak load of the specimens decreased by 36 % from unexposed to 120 min of exposure. Notably, the impact of fire exposure time on peak load was more pronounced than that of the corrosion degree. In terms of cooling methods, the flexural capacities exhibited a more significant decline in beams subjected to water-spraying cooling compared to those under natural cooling, with a reduction of 42 %. The residual flexural capacities of corroded RC beams were predicted using a simplified analytical model incorporating heat transfer analysis and cross-section analysis methods. The predictions demonstrated excellent agreement with the experimental results. The conclusions can provide theoretical guidance for fire rescue after exposure to fire.

Fire resistance time prediction and optimization of cold-formed steel walls based on machine learning

Many full-scale experiments and numerical studies have been conducted to determine the fire performance of cold-formed steel (CFS) walls, but these studies are expensive and time consuming. This study proposes a machine learning (ML) based framework aiming at accurately predicting the fire resistance time (FRT) and optimizing the design of CFS walls under ISO 834 fire condition. To overcome the limitation of 32 experimental data points in the literature, a validated numerical method was used to generate 592 data points to expand the dataset of CFS walls, considering different wall configurations, various sheathing board types and thicknesses. The XGBoost (eXtreme Gradient Boosting) model was trained with numerical data and tested with experimental data. The hyperparameter tuning of the XGBoost model was implemented with Bayesian optimization, and it was found that the XGBoost model accurately predicted the FRT of CFS walls, with R2 and MAPE values being 0.933 and 8.46 %, respectively. The prediction process of the XGBoost model was interpreted by the SHAP (SHapley Additive exPlanations) method to determine the relative importance of input variables. The NSGA-II algorithm was adopted to optimize the FRT-cost dual-objective of CFS walls and the Pareto front including optimal solutions was emerging. The cost for each solution of Pareto front sets was lower than that in the real dataset at same FRT level. This phenomenon implied that the ML-based optimization framework successfully identified the cost-efficient design process. The proposed ML-based optimization framework offers a promising alternative for engineers to design CFS walls effectively with both mechanical and economic objectives.

Experimental study on fire extinguishing performance of typical large space fires on ships

ABSTRACT A reliable fire extinguishing system is an effective measure to reduce the risk of ship fire. Based on a typical large space cabin platform, the experiments on the fire extinguishing performance of different fire extinguishing systems (high-expansion foam, aqueous film-forming foam and water spray) against oil pool fire (6 MW) and wood stack fire (0.3 MW) were carried out. The results show that for oil pool fire, the fire extinguishing performance of aqueous film-forming foam system is higher than that of high-expansion foam system. The fire extinguishing performance of high-expansion foam system is significantly affected by the relative location of high-expansion foam generator, fire source and obstacle. For wood stack fire, the fire extinguishing performance of water spray system is the best, and the fire extinguishing time is only 25 s, followed by the aqueous film-forming foam system (54 s) and the high expansion foam system (66 s).

Numerical Analysis of Combined Effect Hybrid Fibres and Fire Insulation on the Fire Resistance Performance of SCC Beams

The use of self-compacting concrete in structural members has become increasingly prevalent in various construction applications. However, these structures are susceptible to one of the most hazardous disasters, which is the fire. This paper presents a numerical investigation of the fire resistance performance of self-compacting reinforced concrete (SCC) beams, as well as a parametric study to assess the impact of hybrid fibres and fire insulation on enhancing their performance under fire conditions. The study is conducted by developing a 3D finite element (FE) model using ANSYS software, where temperatures are applied according to the ISO834 standard fire. Geometric and material nonlinearities are considered in the evaluation of the behaviour of beams under fire conditions. The developed FE model is validated by comparing the predicted results with those of the experimental tests in literature. The fire resistance performance of SCC beams was compared with that of normal strength concrete (NSC) beams. The parametric study is conducted using three types of fire insulation, namely Tyfo WR-AFP, CAFCO 300, and Carboline Type-5MD. The results showed that SCC beam has lower fire resistance performance than the NSC beam. However, the incorporation of hybrid fibres allows a 17% improvement in the fire resistance of SCC beams. The use of fire insulations has increased the fire resistance time of SCC beams, and more particularly Carboline Type-5MD insulation with an increase of 28%. The combined use of hybrid fibres and fire insulation improved the fire resistance of SCC beams by 43%.

Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts

Abstract Increases in wildfire activity and the resulting impacts have prompted the development of high-resolution wildfire behavior models for forecasting fire spread. Recent progress in using satellites to detect fire locations further provides the opportunity to use measurements toward improving fire spread forecasts from numerical models through data assimilation. This work develops a physics-informed approach for inferring the history of a wildfire from satellite measurements, providing the necessary information to initialize coupled atmosphere–wildfire models from a measured wildfire state. The fire arrival time, which is the time the fire reaches a given spatial location, acts as a succinct representation of the history of a wildfire. In this work, a conditional Wasserstein generative adversarial network (cWGAN), trained with WRF–SFIRE simulations, is used to infer the fire arrival time from satellite active fire data. The cWGAN is used to produce samples of likely fire arrival times from the conditional distribution of arrival times given satellite active fire detections. Samples produced by the cWGAN are further used to assess the uncertainty of predictions. The cWGAN is tested on four California wildfires occurring between 2020 and 2022, and predictions for fire extent are compared against high-resolution airborne infrared measurements. Further, the predicted ignition times are compared with reported ignition times. An average Sørensen’s coefficient of 0.81 for the fire perimeters and an average ignition time difference of 32 min suggest that the method is highly accurate. Significance Statement To initialize coupled atmosphere–wildfire simulations in a physically consistent way based on satellite measurements of active fire locations, it is critical to ensure the state of the fire and atmosphere aligns at the start of the forecast. If known, the history of a wildfire may be used to develop an atmospheric state matching the wildfire state determined from satellite data in a process known as spinup. In this paper, we present a novel method for inferring the early stage history of a wildfire based on satellite active fire measurements. Here, inference of the fire history is performed in a probabilistic sense and physics is further incorporated through the use of training data derived from a coupled atmosphere–wildfire model.

Modeling and Robust Adaptive Practical Predefined Time and Precision Tracking Control of Unmanned Fire Fighting Robot

Modeling and Robust Adaptive Practical Predefined Time and Precision Tracking Control of Unmanned Fire Fighting Robot

Axial compressive behavior of fire‐damaged RC column strengthened with ECC‐CFRP grid

AbstractTo study the axial compressive performance of fire‐damaged reinforced concrete (RC) columns strengthened by engineered cementitious composites (ECC) combined with carbon fiber reinforced polymer (CFRP) grids, eight RC columns were designed and fabricated to analyze the effects of fire exposure time, thickness of the strengthening layer, incorporation of the CFRP grid, and subjecting the strengthening layer to direct compression on the axial compressive performance of the strengthened columns by conducting fire and compressive tests. It was found that the ECC‐CFRP grid strengthening significantly increased the axial compressive load and axial deformation of the columns; the bearing capacity of the strengthened column subjected to fire for a longer period of time was lower under the same strengthening condition; when the thickness of the strengthening layer was increased, the load‐carrying capacity and axial deformation of the column were enhanced; the addition of CFRP grids could further improve the mechanical properties of the repaired column; when the strengthening layer was directly compressed, the axial deformation was significantly reduced, although the bearing capacity would be further increased. An analytical model of the strengthened column was established using the ABAQUS software, and a formula for calculating the bearing capacity of the strengthened column was derived.

Out-of-plane impact behavior and post-impact damage evaluation of GFRP bar-reinforced concrete shear walls in fire

Fiber-reinforced polymers (FRP) have been a prospective material in engineering. However, the attenuation of FRP bar-reinforced concrete members in fire is unclear. This paper studies the impact resistance of glass fiber-reinforced polymer bar-reinforced concrete (GFRP-RC) shear walls under high temperatures. Considering the impacts of strain rate enhancement and high temperature softening on GFRP bars and concrete, a numerical model with finite elements in three dimensions was created. To verify the model, three cases were simulated and compared with the test records, including the seismic performance of FRP-RC shear walls under axial compression, the seismic behavior of reinforced concrete (RC) shear walls under high temperatures, and RC shear walls' impact response. Based on the calibrated numerical model, the displacement, impact force, reaction force, failure mode, energy distribution, internal force and the residual shear bearing capacity of GFRP-RC shear wall subjected to impact under different fire times were analyzed. The influence of fire time on the failure mechanism of GFRP-RC shear walls was studied. Taking the mid-span displacement as the damage index, the evaluation criteria for the four damage levels were determined. The findings indicate that the type of reinforcement has little effect on the shape of impact force, reaction force and displacement time history curve. The peak values of impact force and reaction force of RC walls are larger than those of GFRP-RC shear walls, while the former has smaller peak displacements and residual displacements. The damage of shear walls becomes more severe as the fire time increases, as does the displacement. Also, the impact force, reaction force, shear force and bending moment decrease gradually. The energy absorbed by shear walls is primarily dissipated by concrete, the part by GFRP bars accounting for a small proportion. The post-impact residual bearing capacity of GFRP-RC shear walls is linearly related to the mid-span displacement under impact loads, primarily influenced by high-temperature exposure.

Thermal responses of a concrete slab under hydrogen fuel cell vehicle fires in a semi-open car park

This work aims to investigate the thermal behaviors of the concrete ceiling slab of a semi-open car park exposed to localized fire in hydrogen fuel cell vehicles. For this purpose, a numerical simulation of the hydrogen fuel cell vehicle fire was performed in the Fluid Dynamic Simulator and then coupled with a subsequent thermal analysis of concrete structure carried out in ANSYS Mechanical APDL. In particular, an automatic procedure was used to extract the output of the fire simulation and apply them as boundary conditions of the thermal model. The one-way coupling procedure involving fire simulation and transient thermal analysis has been validated by comparing it with concrete temperatures of a previous test study. Then, two parameters, the diameter of thermal pressure relief devices (1 mm, 2 mm, 3 mm, and 4 mm) and fire spread time between vehicles (0 min, 20 min, and 30 min), are taken into account to study the thermal properties of concrete. The analysis revealed that an increase in the nozzle diameter of the thermal pressure relief device leads to a rise in the maximum concrete surface temperature. The simulation results also showed that the maximum value of the heat release rate increases with a higher value of the nozzle diameter of the thermal pressure relief device and a shorter fire spread time between vehicles.

DETERMINATION OF THE CLASS B FIRE EXTINGUISHING PARAMETERS USING SPRAYED WATER

Sprayed water is a versatile tool for localising and extinguishing fires of various types. When water evaporates, its volume increases by 1700 times, resulting in the dilution of combustible components with non-combustible water vapour. The study aims to determine the parameters of extinguishing class B fires using sprayed water by obtaining analytical dependencies for these parameters. The paper derives an expression for the extinguishing temperature of class B fires when extinguished with sprayed water, using Semenov’s conditions. The process of liquid combustion is considered as diffusion. The study shows that the extinguishing temperature of this type of fire is a function of the activation energy of the burning liquid and the initial temperature (at the time of sprayed water supply) of the flame. A parameter of this function is the universal gas constant. The authors constructed a graph showing the area of change in the fire extinguishing temperature. The difference between the initial flame temperature and the fire extinguishing temperature belongs to the range of (80÷120) °C. Using the transition function of class B fire, the study derives an expression for its extinguishing time, which depends on the initial flame temperature, activation energy, and the multiplicative component in the form of a fire time constant. For the relative extinguishing time, the authors constructed the area of change in this parameter depending on the activation energy and the initial temperature. It shows that the value of the extinguishing time of such a fire is (0.28÷0.32) of the value of the constant fire time. The paper presents an expression for the minimum value of the intensity of the sprayed water supply, which ensures extinguishing a class B fire. This intensity of the supply of sprayed water, reduced to the value of the fire transmission coefficient, is determined by the initial temperature of the flame and the activation energy of the liquid that burns. The authors constructed the area of change of this fire extinguishing parameter. It shows that the minimum value of the intensity of the supply of sprayed water belongs to the range (3.8÷7.8) per unit of the fire transmission coefficient. Keywords: fire, fire parameters, sprayed water.

Forecasting Hourly Wildfire Risk: Enhancing Fire Danger Assessment Using Numerical Weather Prediction

Abstract Wildfire agencies use fire danger rating systems (FDRSs) to deploy resources and issue public safety measures. The most widely used FDRS is the Canadian fire weather index (FWI) system, which uses weather inputs to estimate the potential for wildfires to start and spread. Current FWI forecasts provide a daily numerical value, representing potential fire severity at an assumed midafternoon time for peak fire activity. This assumption, based on typical diurnal weather patterns, is not always valid. To address this, we developed an hourly FWI (HFWI) system using numerical weather prediction. We validate HFWI against the traditional daily FWI (DFWI) by comparing HFWI forecasts with observation-derived DFWI values from 917 surface fire weather stations in western North America. Results indicate strong correlations between forecasted HFWI and the observation-derived DFWI. A positive mean bias in the daily maximum values of HFWI compared to the traditional DFWI suggests that HFWI can better capture severe fire weather variations regardless of when they occur. We confirm this by comparing HFWI with hourly fire radiative power (FRP) satellite observations for nine wildfire case studies in Canada and the United States. We demonstrate HFWI’s ability to forecast shifts in fire danger timing, especially during intensified fire activity in the late evening and early morning hours, while allowing for multiple periods of increased fire danger per day—a contrast to the conventional DFWI. This research highlights the HFWI system’s value in improving fire danger assessments and predictions, hopefully enhancing wildfire management, especially during atypical fire behavior.

Effect of Inter-Hole Delays on Seismic Safety with Regard to Firing Time Deviation

Effect of Inter-Hole Delays on Seismic Safety with Regard to Firing Time Deviation