Severe Casualties Research Articles

Noise-robust pipe wall-thinning discrimination system using convolution recurrent neural network model

Pipe wall-thinning is a phenomenon whereby the thickness of pipes in a nuclear power plant decreases over time owing to extended operational years. This thickness reduction is caused by various long-term thermal aging mechanisms such as Flow-Accelerated Corrosion, Liquid Droplet Impingement Erosion, cavitation, and flashing. Reducing the thickness of the secondary system pipes to the point of rupture can lead to severe human casualties and significant economic losses. Consequently, domestic power plant operators regularly manage the power plant pipes. Ongoing research focuses on the identification and management of pipe wall-thinning. However, previous studies have encountered problems in accurately judging the decrease in pipe wall-thinning in the presence of noise. To overcome this, Convolutional Neural Network (CNN) models based on image feature analysis have been used. This approach allows for the differentiation of pipe wall-thinning feature from small-sized noise in a single image. However, there were difficulties in making accurate judgments for large-sized noise that resembled the feature of pipe wall-thinning. This paper aims to analyze the limitations of the current pipe wall-thinning evaluation methods and to achieve accurate pipe wall-thinning discrimination through time-series analysis of continuous pipe wall-thinning data. The proposed method employs a Convolutional Recurrent Neural Network (CRNN) model, integrating the Recurrent Neural Network(RNN) model with the CNN model. The image feature of the pipes, extracted using CNN, are utilized as inputs for the RNN. This enables the observation of how the image features of the pipes change over time. This feature differentiates from the time-series feature of noise that occurs suddenly. Through this method, the paper proposes a new approach for effectively identifying the gradual decrease in pipe wall-thinning, enabling precise assessment of pipe wall-thinning progression.

Fault diagnosis of nuclear power plant sliding bearing-rotor systems using deep convolutional generative adversarial networks

Sliding bearings are crucial rotating mechanical components in nuclear power plants, and their failures can result in severe economic losses and human casualties. Deep learning provides a new approach to bearing fault diagnosis, but there is currently a lack of a universal fault diagnosis model for studying bearing-rotor systems under various operating conditions, speeds and faults. Research on bearing-rotor systems supported by sliding bearings is limited, leading to insufficient fault data. To address these issues, this paper proposes a fault diagnosis model framework for bearing-rotor systems based on a deep convolutional generative adversarial network (TF-DLGAN). This model not only exhibits outstanding fault diagnosis performance but also addresses the issue of insufficient fault data. An experimental platform is constructed to conduct fault experiments under various operating conditions, speeds and faults, establishing a dataset for sliding bearing-rotor system faults. Finally, the model's effectiveness is validated using this dataset.

The Social, Economic, and International Relations Impact of the 2023 Israel-Hamas War

Hamas's attack on Israel on October 7th, 2023, is not the first of its type. The Israeli-Palestine tension can be traced back to 1947 following the UN Partition Plan that divided the British Mandate of Palestine into Jewish and Arab Nations. Following Britain’s withdrawal from the Peninsula, the Jewish leaders declared the State of Israel, sparking the first Arab-Israeli conflict and this tension has worsened over the years. We use Thomas Hobbes's Social Contract to explain the tension between both parties. According to Hobbes, man lives in a State of Nature which is characterized by war and the continuous fear of death. This fear of death is the reason why man enters into a contract (Social Contract). There have been diverse schools of thought as to the origin of the Israeli-Palestine tension. Some believe it is the 1947 UN Partition Plan, others believe it is the June 1967 war, while others believe it is religion. Regardless of the original cause, it is evident that both parties have retained severe casualties over the years. Hence, the purpose of this paper is to review the economic, social, and international relations consequences of the 2023 Israel-Hamas war. The current war has had several social consequences: refugee migrations, lack of basic survival necessities, withdrawal of medical aid groups, starving refugees, desolated cities, and allegations of rape and sexual torture. Economically, the war has led to the loss of income from economic activities, an increase in oil prices, reignited inflation, high-interest rates, increased government expenditures, and exchange rates. It has also impacted international relations among nations due to a split in support between Israel and Palestine. Nevertheless, there is an allowance for further study because this paper is limited only to the social, economic, and international relations impact of the war.

Analysis of faulting destruction and water supply pipeline damage from the first mainshock of the February 6, 2023 Türkiye earthquake doublet

In 2023, two consecutive earthquakes exceeding a magnitude of 7 occurred in Türkiye, causing severe casualties and economic losses. The damage to critical urban infrastructure and building structures, including highways, railroads, and water supply pipelines, was particularly severe in areas where these structures intersected the seismogenic fault. Critical infrastructure projects that traverse active faults are susceptible to the influence of fault movement, pulse velocity, and ground motions. In this study, we used a unique approach to analyze the acceleration records obtained from the seismic station array (9 strong ground motion stations) located along the East Anatolian Fault (the seismogenic fault of the MW7.8 mainshock of the 2023 Türkiye earthquake doublet). The acceleration records were filtered and integrated to obtain the velocity and displacement time histories. We used the results of an on-site investigation, jointly conducted by China Earthquake Administration and Türkiye’s AFAD, to analyze the distribution of PGA, PGV, and PGD recorded by the strong motion array of the East Anatolian Fault. We found that the maximum horizontal PGA in this earthquake was 3.0 g, and the maximum co-seismic surface displacement caused by the East Anatolian Fault rupture was 6.50 m. As the fault rupture propagated southwest, the velocity pulse caused by the directional effect of the rupture increased gradually, with the maximum PGA reaching 162.3 cm/s. We also discussed the seismic safety of critical infrastructure projects traversing active faults, using two case studies of water supply pipelines in Türkiye that were damaged by earthquakes. We used a three-dimensional finite element model of the PE (polyethylene) water pipeline at the Islahiye State Hospital and fault displacement observations obtained through on-site investigation to analyze pipeline failure mechanisms. We further investigated the effect of the fault-crossing angle on seismic safety of a pipeline, based on our analysis and the failure performance of the large-diameter Thames Water pipeline during the 1999 Kocaeli earthquake. The seismic method of buried pipelines crossing the fault was summarized.

Aceh's tsunami wave evolution and its interaction with hybrid protection structure

The 2004 Aceh tsunami tragedy was one of the most catastrophic occurrences, resulting in damage and severe casualties in multiple countries. This study proposes a hybrid coastal protection system made up of mangroves, a sea dike, a trench, or a combination of the three structures to prevent similar devastation in the future. This system is expected to reduce the tsunami wave height, thus lowering their potential damage. The tsunami wave propagation is reproduced using a nonlinear shallow water equation model. To construct a numerical scheme, a staggered grid finite volume method is implemented. This scheme is then validated using several benchmark tests. Once validated, the computational results are compared to experimental data collected at the Laboratory of Coastal Dynamics in Yogyakarta, Indonesia. Both the physical and numerical models use the downscaled Aceh tsunami waves and real bathymetry. Several scenarios of structures combination are presented with the aim to determine the most effective combination. A sensitivity analysis is also conducted to support the results.

Improving Traffic Accident Severity Prediction Using Convoluted Features and Decision-Level Fusion of Models

Although there have been improvements in traffic safety measures, the frequency of traffic accidents continues to persist. Developing countries experience a significant impact from traffic accidents with respect to fatalities and property damage. Traffic accidents happen for multiple reasons, involving traffic conditions, driving violations, driver misjudgments, and so forth. Severe casualties may lead to fatalities; therefore, accident severity prediction might help reduce the chances of fatalities. This research makes use of a U.S. road accident dataset that contains the most relevant 32 factors related to accidents. For obtaining accurate prediction of traffic accident severity, this research proposes a solution based on an ensemble of random forest and support vector classifiers that is trained using deep convoluted features. Features are extracted from the road accident dataset using a convolutional neural network (CNN). The performance of models using original features and CNN features is analyzed that shows the superiority of convoluted features. Experimental results involving the use of several well-known machine learning models indicate that the proposed model can obtain an accuracy of 99.99% for traffic accident severity prediction. The efficacy of the proposed model is validated against existing state-of-the-art approaches.

Accident Probability Prediction and Analysis of Bus Drivers Based on Occupational Characteristics

A city bus carries a large number of passengers, and any traffic accidents can lead to severe casualties and property losses. Hence, predicting the likelihood of accidents among bus drivers is paramount. This paper considered occupational driving characteristics such as cumulative driving duration, station entry and exit features, and peak driving times, and categorical boosting (CatBoost) was used to construct an accident probability prediction model. Its effectiveness was confirmed by the daily management data of a Chongqing bus company in June. For data processing, Multiple Imputation by Chained Equations for Random Forests (MICEForest) was used for data filling. In terms of prediction, a comparative analysis of four boosted trees revealed that CatBoost exhibited superior performance. To analyze the critical factors affecting the probability of bus driver accidents, SHapley Additive exPlanations (SHAP) was applied to visualize and interpret the results. In addition to the significant effects of age, rainfall, and azimuthal change, etc., we innovatively discovered that the proportion of driving duration during peak duration, the dispersion when entering and exiting stations, the proportion of driving duration within a week, and the accumulated driving duration of the previous week also had varying degrees of impact on accident probability. Our research and findings provide a new idea of accident prediction for professional drivers and direct theoretical support for the accident risk management of bus drivers.

Spatiotemporal Evaluation of Injury Severity of Expressway Rear-End Crashes in China: Insights Using Random Parameters Approaches

Rear-end crashes have become an urgent problem in China, resulting in severe casualties and massive property damage. This study aims, first, to discover the determinants affecting injury severity in expressway rear-end crashes and to examine the corresponding changes over time and space, and, second, to identify the sources of unobserved heterogeneity shifts, which could have substantial implications for efficient and effective crash prevention. Using three-year crash data (2017 -2019) from two expressways with speed limits of 120 km/h and 100 km/h (G2 and G25) in Jiangsu and Guangdong provinces in China, four random parameter logit model (RP-LM) approaches were used in this study to analyze the contributing factors: fixed parameter LM, RP-LM, RP-LM with heterogeneity in means, and RP-LM with heterogeneity in means and variances. With three injury severity consequences (severe injury, minor injury, and no injury), the characteristics of the driver, vehicle, roadway, environment, and others were proposed as possible determinants. The temporal and spatial stability were then explored using transferability tests. The marginal effects were computed to explore further potential heterogeneity and spatiotemporal variations. The estimated results indicate the superiority of the proposed model over its base counterparts, with very good [Formula: see text] values all over 0.64. Saturday, early morning, and winter indicators were identified as significant random parameters, and several variables were observed to manifest heterogeneity in means and variances. Speeding behavior, early morning, and heavy truck indicators increased the likelihood of minor and severe injury. These findings could significantly improve expressway safety related to rear-end crashes.

Collapse Resistance of Composite Structures with Various Optimized Beam–Column Connection Forms

Steel–concrete composite structures are widely used in composite frame structures and super high-rise buildings. However, the lack of relevant building design standards to ensure their structural stability under extreme conditions has led to potential failures in beam–column connections due to excessive loads. These failures can trigger the progressive collapse of high-rise buildings, resulting in severe casualties. In this study, a comparative numerical analysis was conducted to evaluate the collapse resistance of composite structures in the event of a middle-column loss scenario, focusing on six commonly used beam–column connections. The results show that while the six connections exhibit minimal differences under normal operating conditions, they display significant variations when subjected to extreme loads. Furthermore, a design concept is proposed to enhance the collapse capacity of these structures, and its effectiveness is validated via analysis.

Development of a Data-Based Machine Learning Model for Classifying and Predicting Property Damage Caused by Fire

Large fires in factories cause severe human casualties and property damage. Thus, preparing more economical and efficient management strategies for fire prevention can significantly improve fire safety. This study deals with property damage grade prediction by fire based on simplified building information. This paper’s primary objective is to propose and verify a framework for predicting the scale of property damage caused by fire using machine learning (ML). Korean public datasets are collected and preprocessed, and ML algorithms are trained with only 15 input data using building register and fire scenario information. Four models (artificial neural network (ANN), decision tree (DT), k-nearest neighbor (KNN), and random forest (RF)) are used for ML. The RF model is the most suitable for this study, with recall and precision of 74.2% and 73.8%, respectively. Structure, floor, causes, and total floor area are the critical factors that govern the fire size. This study proposes a novel approach by utilizing ML models to accurately and rapidly predict the size of fire damage based on basic building information. By analyzing domestic fire incident data and creating fire scenarios, a similar ML model can be developed.

Optimizing Mass Casualty Triage: Using Discrete Event Simulation to Minimize Time to Resuscitation.

Urban areas in the US are increasingly focused on mass casualty incident (MCI) response. We simulated prehospital triage scenarios and hypothesized that using hospital-based blood product inventories for on-scene triage decisions would minimize time to treatment. Discrete event simulations modeled MCI casualty injury and patient flow after a simulated blast event in Boston, MA. Casualties were divided into moderate (Injury Severity Score 9 to 15) and severe (Injury Severity Score >15) based on injury patterns. Blood product inventories were collected from all hospitals (n = 6). The primary endpoint was the proportion of casualties managed with 1:1:1 balanced resuscitation in a target timeframe (moderate, 3.5 U red blood cells in 6 hours; severe, 10 U red blood cells in 1 hour). Three triage scenarios were compared, including unimpeded casualty movement to proximate hospitals (Nearest), equal distribution among hospitals (Equal), and blood product inventory-based triage (Supply-Guided). Simulated MCIs generated a mean ± SD of 302 ± 7 casualties, including 57 ± 2 moderate and 15 ± 2 severe casualties. Nearest triage resulted in significantly fewer overall casualties treated in the target time (55% vs Equal 86% vs Supply-Guided 91%, p < 0.001). These differences were principally due to fewer moderate casualties treated, but there was no difference among strategies for severe casualties. In this simulation study comparing different triage strategies, including one based on actual blood product inventories, nearest hospital triage was inferior to equal distribution or a Supply-Guided strategy. Disaster response leaders in US urban areas should consider modeling different MCI scenarios and casualty numbers to determine optimal triage strategies for their area given hospital numbers and blood product availability.

Evaluation of disaster-bearing capacity for natural gas pipeline under third-party damage based on optimized probabilistic neural network

Third-party damage has become a significant factor leading to pipeline failures and leaks, resulting in severe ecological damage and human casualties. Traditional safety risk analysis methods in the past have focused on severing the causal chain of accidents, while neglecting the unique characteristics of different pipeline segments and environmental factors on their ability to bearing disasters. This paper presents a framework for evaluating the disaster-bearing capacity of natural gas pipeline third-party damage based on probabilistic neural networks. A comprehensive disaster-bearing capacity index system for evaluating third-party damage to natural gas pipelines has been constructed by analyzing the causal factors and potential consequences of third-party damage risk incidents. Next, the cluster analysis and indicator weighting information is used for achieving the comprehensive disaster-bearing capacity assessment. Finally, the data samples are constructed with the index levels and comprehensive disaster-bearing capacity levels of each evaluation unit. An optimization algorithm, Particle Swarm Optimization (PSO), is utilized to improve the parameter selection process of the Probabilistic Neural Network (PNN) model, in order to evaluate the disaster-bearing level of each unit. A typical long-distance natural gas pipeline in Zhejiang Province was selected as the case study, and a total of 200 research units were obtained through pipeline analysis. Furthermore, 14 indicator data were obtained by leveraging multiple sources of data surrounding the pipeline. With the utilization of cluster analysis, it was determined that Gaussian mixed model clustering method ultimately identified a total of 5 different levels of disaster-bearing capacity. The optimal smoothing factor can be accurate from 0.5 to 0.588 by using the PSO-PNN model, and the final accuracy rate is improved from 88% to 98%, which is obviously the introduction of PSO optimization algorithm in the PNN model can improve the accuracy of the model. This represents a significant improvement in accuracy when compared to traditional PNN models. The proposed evaluation method can provide scientific basis for the prevention and control of third-party damage in oil and gas pipelines and pipeline planning.

Prediction of Storm Surge Water Level Based on Machine Learning Methods

Storm surge disasters result in severe casualties and economic losses. Accurate prediction of storm surge water level is crucial for disaster assessment, early warning, and effective disaster management. Machine learning methods are relatively more efficient and straightforward compared to numerical simulation approaches. However, most of the current research on storm surge water level prediction based on machine learning methods is primarily focused on point predictions. In this study, we explore the feasibility of spatial water level prediction using the ConvLSTM model. We focus on the coastal area of Guangdong Province and employ MIKE21(2019) software to simulate historical typhoons that have made landfall in the region from 1991 to 2018. We construct two datasets: one for direct water level prediction and the other for indirect water level prediction based on water level changes. Utilizing the ConvLSTM network, we employ it to forecast storm surges on both datasets, effectively capturing both temporal and spatial characteristics and thus ensuring the production of dependable results. When directly predicting water levels, we achieve an MAE (mean absolute error) of 0.026 m and an MSE (mean squared error) of 0.0038 m2. In contrast, the indirect prediction approach yields even more promising results, with an MAE of 0.014 m and an MSE of 0.0007 m2. Compared to traditional numerical simulation methods, the ConvLSTM-based approach is simpler, faster, and able to predict water levels accurately without boundary conditions or topographies. Furthermore, we consider worst-case scenarios by predicting the maximum water increase value using the random forest model. Our results indicate that the random forest model can serve as a valuable reference for forecasting the maximum water increase value of typhoon storm surges, supporting effective emergency responses to disasters.

Analysis of Peak Ground Acceleration and Seismogenic Fault Characteristics of the Mw7.8 Earthquake in Turkey

A Mw7.8 earthquake struck Turkey on 6 February 2023, causing severe casualties and economic losses. This paper investigates the characteristics of strong ground motion and seismogenic fault of the earthquake. We collected and processed the strong ground motion records of 379 stations using Matlab, SeismoSignal, and Surfer software: Matlab (Version R2016a), SeismoSignal (Version 5.1.0), and Surfer (Version 23.0.15), and obtained the peak ground acceleration (PGA) contour map. We analyzed the near-fault effect, the fault locking segment effect, and the trampoline effect of the earthquake based on the spatial distribution of PGA, the fault geometry, and slip distribution. We found that the earthquake generated a very strong ground motion concentration effect in the near-fault area, with the maximum PGA exceeding 2000 cm/s2. However, the presence of fault locking segments influenced the spatial distribution of ground motion, resulting in four significant PGA high-value concentration areas at a local dislocation, a turning point, and the end of the East Anatolian Fault. We also revealed for the first time the typical manifestation of the trampoline effect in this earthquake, which was characterized by a large vertical acceleration with a positive direction significantly larger than the negative direction. This paper provides an important reference for understanding the seismogenic mechanism, damage mode, characteristics, and strong earthquake law of the Turkey earthquake.

The Dynamics and Microphysical Characteristics of the Convection Producing the Record-Breaking Hourly Precipitation on 20 July 2021 in Zhengzhou, China

An hourly rainfall of 201.9 mm fell in Zhengzhou on 20 July 2021, breaking the hourly rainfall record of mainland China and causing severe urban flooding and human casualties. This observation-based study investigates the associated convective-scale and mesoscale dynamics and microphysical processes using disdrometer and polarimetric radar observations aided by retrievals from the Variational Doppler Radar Analysis System. The synoptic flow forcing brought abundant moisture from the oceans and converged at Zhengzhou; then, the extreme rainfall was produced by a slow-moving convective storm that persisted throughout the hour over Zhengzhou. Unusually high concentrations of raindrops of all sizes (showing combined properties of maritime and continental convection) are revealed by the disdrometer data, whereas the polarimetric radar data suggest that both ice-based and warm rain processes were important contributors to the total rainfall. High precipitation efficiency was achieved with an erect updraft at the low levels, whereas enhanced easterly inflows kept the storm moving slowly. The interaction between convective-scale and mesoscale dynamics and microphysical processes within the favorable synoptic conditions led to this extremely heavy rainfall.

Investigation of seismic performance of indoor AAC infill walls with flat‐truss bed‐joint reinforcement

AbstractIntroductionAutoclaved Aerated Concrete (AAC) is a lightweight and energy‐efficient material which makes it one of the most convenient materials to be used in infill walls. Accordingly, AAC walls are becoming more and more favored infill systems in earthquake‐prone regions. Although the infills are not considered primary components in seismic design, they are highly susceptible to be damaged under seismic actions in in‐plane (IP) and out‐of‐plane (OOP) directions.ObjectivePast post‐earthquake site surveys indicated that the failure of infill walls may result in severe casualties, injuries, and economic losses. In addition, damage to infills may hinder the functionality of critical buildings, which are expected to be in service after earthquakes. The application of horizontal bed‐joint reinforcement between infill courses is one of the promising solutions to increase the seismic resilience of infill walls.MethodologyThis paper presents the experimental performance investigation of AAC infill walls with flat‐truss bed‐joint reinforcement being subjected to lateral loading in IP and OOP directions. The specimens represent relatively thin infills with a thickness of 150 mm, which are generally used in indoor environments. To consider the seismic actions in the IP direction, two full‐scale infill wall specimens (one unreinforced reference and one with flat‐truss reinforcement) built in reinforced concrete frames were first tested under reversed cyclic IP displacement reversals and then tested under the cyclic OOP displacements.ConclusionsThe results revealed that the use of flat‐truss reinforcement enhanced the OOP strength, ultimate displacement, and energy dissipation capacity of infill walls that have been subjected to cyclic IP displacements previously.

Study of seismic vulnerability of steel frame structures on soft ground considering group effect

Earthquakes have caused increasingly severe casualties and economic losses in coastal urban areas where soft soils are widely distributed. To mitigate disaster losses, this paper aims to investigate the mechanism of the group effect, the interaction between a group of buildings (at least three) through the foundation, on seismic vulnerability in coastal urban areas. Firstly, the analysis models are established, and the validity is subsequently verified. Meanwhile, a self-built automatic iterative calculation program for multilayer soils is adopted to consider the nonlinearity of the soft soils. On this basis, probabilistic seismic demand analyses are further performed using ten earthquake ground motions. The seismic vulnerability formulas of the structures are obtained in combination with the probabilistic seismic demand models. Finally, the influence of the group effect on the seismic performance of the structures is evaluated from different perspectives based on the vulnerability formulas. The results indicate that the group effect of the steel frame structures makes the performance margin ratio (PMR) significantly higher and reduces the seismic vulnerability of the structures in coastal urban areas. The mechanism revealed in this paper presents a more accurate way to conduct earthquake-resistant design, provides guidelines for setting the criteria, and is likewise instructive to the seismic design of other structural forms.

Surface deformation and damage of 2022 (M6.8) Luding earthquake in China and its tectonic implications

AbstractThe 2022 (M6.8) Luding earthquake on the Xianshuihe Fault Zone (XFZ) caused severe casualties and property losses, and surface deformation and damage of which is crucial for studying the earthquake hazard assessment. However, few intensive scientific understanding has obtained to date because of widespread coronavirus transmission, strong vegetation coverage, and post-earthquake paralyzed traffic. By integrating high-resolution satellite images, large-scale geomorphic mapping, and UAV surveys, we constrain coseismic fractures and ruptures along an NW-SE-trending surface deformation zone, with discontinuous geomorphic scarps,en echeloncracks, and bulges concentrated in the areas of Yanzigou, Moxi, Menghugang, and Xingfu villages near the epicenter. Field observation also shows that the zone extends nearly parallel to the pre-existing XFZ with a length of ∼35 km with variable widths and a maximum vertical displacement of ∼100 ± 10 cm. The earthquake-induced surface coseismic effects, such as landslides, rock falls, and collapses, caused damage to the area. The amplification effect of the topography and the improper aseismic design and poor constructions may be responsible for the spatial distribution of MM Intensity IX, which is larger than other previous earthquakes that occurred in the surrounding area with a similar tectonic setting.

Application of Deep Neural Network for Gas Source Localization in an Indoor Environment

Nowadays, the quality of air in the environment has been impacted by the industry. It is important to make sure our ambient air especially in an indoor environment is clean from contaminating particles or harmful gases. Therefore, the air quality inside the indoor environment should be monitored regularly. One of the major problems, when a particular environment has been contaminated by harmful gases, is finding the source of the emission. If the indoor environment has been contaminated by a harmful source it should be instantly localized and eliminated to prevent severe casualties. In this paper, we propose the utilization of synthetic data generated by the Computational Fluid Dynamic (CFD) approach to train the Deep Neural Network (DNN) model called CFD-DNN to perform gas source localization in an indoor environment. The model is capable to locate the contaminated source within a small area of an indoor environment. A total of 361 datasets with different locations of contaminated source release have been obtained using the CFD approach. The obtained dataset was divided into training and testing datasets. The training dataset was used for the model training process while the testing dataset is fed into the model to test model reliability to predict the gas source location. The Euclidian distance equation was used to measure the distance error between the actual and predicted location of the source. The result shows that the model is capable to locate the gas source within a minimum and maximum error of 0.03m to 0.46m respectively.

Characteristic Analysis of Four Major Nighttime Fire Cases on Fire Safety of Long-Term Care Institutions Using Fire Protection Defense-In-Depth Strategy

Providing high-quality care services and fire safety for long-term care institutions is an important issue in Taiwan, which became an aging society in 2018. The fire incidents in Taiwan over the years show that nighttime fires in care institutions often cause serious casualties. It is necessary not only to understand the causes of serious nighttime fire incidents that have occurred but also to draw lessons from the fires that have been put out without causing injuries. In this study, the top two serious nighttime fire accidents in long-term care institutions in the past two decades in Taiwan were analyzed based on the publicly official and academic literature utilizing fire protection defense-in-depth strategies. For comparison, two other nighttime fire cases with similar scenarios but no casualties were also analyzed in depth about the cause of no casualties. The buildings of the four nighttime fires were equipped with fire protection equipment in their public areas. The theoretical basis of the research is the fire protection defense-in-depth strategy. In both categories of severe casualties and no severe casualties, one was caused by arson and the other one by an electrical fire, with the ignition point of a fire in the storeroom and the other in the ward. However, the end results were quite different. The analyzed results showed that the severe fires lasted for about an hour, while the fires without casualties were put out within 15 min. A well-constructed second layer of defense measures could effectively contain a fire, and an effective third layer of measures could avoid casualties. The death rate of personnel can be reduced from a dozen to zero, and the burning time is also greatly reduced. The results could be used as a reference for emergency measures in long-term care institutions.