Fire Technology最新文献

This study develops random forest models to interrogate housing survival in the 2021 Marshall Fire, investigating the role of housing, parcel, and neighborhood characteristics. This grass fire affected suburban communities and destroyed more than 1,000 houses. The authors compiled a data set consisting of all the destroyed houses, along with damaged and standing neighboring houses. After removing houses with insufficient data, 1055 impacted houses were used to develop models for each of the three impacted jurisdictions and for the full data set of 1055 houses. In addition, model versions were developed that use only the subset of predictor characteristics available pre-fire. The pre-fire model results showed that the five housing characteristics that resulted in the largest increase in mean square error (MSE) when randomly permutated were predominantly neighborhood and parcel level characteristics. All predictors resulting in percent increases in MSE of 15% or greater were neighborhood level characteristics. Parcel and community characteristics encompass 78%, 86%, 100%, and 80% of predictors resulting in percent increase in MSE greater than 5% for the models developed for Louisville, Superior, Unincorporated Boulder County, and all data, respectively. Additionally, the identification of the most important parameters showed that the majority of the most impactful variables were not within the homeowner’s control. This conclusion demonstrates the importance of neighborhood and community characteristics on housing survival that are controlled by the jurisdiction, especially in a home rule state where building codes and planning may differ across jurisdictional boundaries. We found little reduction in model accuracy (%-change in balanced accuracy under 12%), when only pre-fire variables were considered. Taken together, these results suggest a crucial role for jurisdiction or community mitigation of risk in WUI areas.

Thermal protective performance of protective clothing is greatly affected by structural parameters of fabric, air gap, and orientation of testing. This paper used Box-Behnken design experimental design to see the influence of pick density of shell (outer) layer, air gap, and orientation of test on thermal protective performance in terms of protection time. The testing was done at constant radiant heat flux. Thermal protective performance was measured in terms of second degree burn time using Stoll’s curve. The model showed F value of 72.98 and p-value 0.0001 which shows that model was significant. It was also found that there was significant effect of pick density, air gap, and orientation angle on thermal protective performance. There was positive effect of pick density and air gap but negative effect of orientation angle. This was also observed that effect of pick density was more when air gap increases. The study can help in development of thermal protective clothing for different parts of body.

To address the challenges of low detection accuracy, slow detection speed, coarse feature extraction, and the difficulty of detection deployment in complex forest fire backgrounds, this paper presents a forest fire object detection algorithm based on an improved YOLOv5s (YOLOv5s-ACE). The algorithm not only realizes the accurate identification of small objects, but also guarantees the accuracy and speed of detection. Firstly, YOLOv5s-ACE uses Copy-Pasting data enhancement to expand the small object sample set to reduce the overfitting risk in the process of model training. Secondly, it choose Atrous Spatial Pyramid Pooling (ASPP) to replace Spatial Pyramid Pooling (SPP) module in backbone part of YOLOv5 network. Therefore, the proposed algorithm can enlarge the receptive field while ensuring the resolution, which is conducive to the accurate positioning of small object forest flame. Third, after adding the Convolutional Block Attention Module (CBAM) module to the C3 module of the Neck layer, the key features of the forest flame object can be further screened, while irrelevant information that interferes with the flame detection, such as background information, can be eliminated. The network performance of forest fire detection is improved without increasing the depth, width and resolution of the input image. Finally, we replace CIOU losses (Complete-IoU) with EIOU losses (Efficient-IoU) to optimize the performance of the model and improve accuracy. The experimental results show that compared with the original algorithm, the proposed object detection algorithm improves mean Average Precision (mAP) by 5.6%, Precision by 2.7%, Recall by 6.5% and GFlops by 6.7%. Even compared with the YOLOv7 algorithm, the proposed algorithm YOLOv5s-ACE increases mAP by 0.9%, Precision by 2.2%, and Recall by 0.3%.

In the article, a state analysis of the organic carbon-containing additives use in fire fighting has been carried out. Negative environmental effects when using fluorine-containing short- and long-chain surfactants, which can act as both a fire extinguishing agent and its decomposition product, have been noted. As an important direction for further evolution in the field of fire extinguishing agents, the use of oxysilanes and gel systems based on liquid glass as environmentally friendly compounds has been noted. The prospects and environmental friendliness of the use of acoustic effects in extinguishing fires have been noted. The addition of small amounts of inorganic and organic compounds is regarded as an inexpensive and effective method to increase the fire fighting properties of water. In this work, the ecological characteristics of a number of organic compounds used in fire fighting as thickeners of aqueous solutions and the reduction of their surface tension were studied. Alginic acid has been shown to be the most environmentally friendly water additive among the investigated organic carbon-containing compounds used in fire fighting.

Graphical Abstract

Engineered timber is increasingly in demand for tall buildings due to its positive impact on building sustainability. However, quick adoption raises fire engineering questions regarding flammability and structural performance. Understanding the behaviour of timber in fire is crucial, particularly for structural calculations of tall buildings. The charring rate of timber plays a significant role in its structural performance because the loss of cross section reduces the load bearing capacity of the element. Eurocode-5 (EC5) provides a simple method to calculate the charring rate and it is widely adopted for design in many countries while more complex physics-based models exist but are rarely used for design. For example, we want to know when EC5 underpredicts or overpredicts and by how much. This paper compares different data-driven methods, including statistical and artificial intelligence algorithms, for predicting the average charring rate of timber in fire. A new database of charring rates, VAQT, was created comprised of 231 furnace tests of timber products found in the scientific and technical literature. Statistical methods such as ridge regression (λ = 0.001) predict the charring rate with a minimum 11% error whilst EC5 predicts with 27% error. A trained neural network predicts the charring rate with minimum 9% error. This paper presents a novel database of timber charring experiments and provides a set of data-driven predictive models, all of which calculate the average charring rate with a significantly higher accuracy than EC5 for a wide range of mass timber products.

While normal weight aggregate concrete (NWAC) can experience significant strength loss and spalling at high temperatures, lightweight aggregate concrete (LWAC) can maintain its structural integrity. Stress–strain relationship of concrete is an important test to perform during designing phase of concrete infrastructures. Therefore, this study focuses on exploring the stress–strain behavior of NWAC and LWAC under uniaxial compression at temperatures ranging from 20 to 750°C. In addition, pruning long short-term memory (P-LSTM) networks to create a predictive model for the stress–strain relationship of NWAC and LWAC is also utilized. Concrete mixture designs containing ordinary Portland cement, silica fume, and lightweight expanded clay aggregate, were first optimized to reduce the number of experiments using the response surface method. Subsequently, 30 mixture designs were fabricated and subjected to compression tests, following exposure to varying temperatures that ranged from 20 to 750°C, to evaluate their stress–strain relationship and determine associated mechanical properties. Experimental results were then utilized to develop a P-LSTM model used to forecast the stress–strain relationship of concrete at varying temperatures. The P-LSTM model developed in this study improved the prediction accuracy and stability beyond conventional LSTM model, which would be useful in the design and optimization of NWAC and LWAC structures. Additionally, the P-LSTM model has a lower computational cost and less likelihood of over-fitting as compared to typical LSTM networks.