Earthquake Casualties Research Articles

Probabilistic assessment of earthquake casualties in residential areas

Accurately and reasonably assessing earthquake casualties is crucial for seismic disaster planning and governmental decision-making. However, current research on earthquake casualty assessments often lacks sufficient consideration of the uncertainties related to seismic scenarios and influencing factors. To enhance the authenticity and reliability of casualty assessments, this study proposes a seismic casualty assessment model capable of simultaneously considering multiple influencing factors. It quantitatively evaluates each influencing factor and comprehensively integrates the uncertainty characteristics associated with seismic occurrence time, building damage states, and the number of occupants. Using a residential area as an example, the research compares casualty assessment results with and without considering apartment layouts and the age structure of residents. It also explains the probabilistic method for quantifying casualties and constructing vulnerability models for seismic events in the residential area. Additionally, a simplified analytical method applicable to probabilistic assessment of earthquake casualties in masonry residential areas in Beijing is proposed. The research findings indicate that the apartment layouts within buildings have a relatively minor impact on the assessment results of casualties in the residential area, while the age structure of residents significantly affects earthquake casualty estimates. Casualty rates in the residential area, considering uncertainties in the three factors, approximately follow a log-normal distribution. For seismic intensities ranging from VI(0.05g) to IX(0.40g), the standard deviation of casualty rates across different seismic intensities exceeds 0.4. The probabilistic analysis method proposed in this study enhances existing models for earthquake casualty assessments and addresses the shortcomings in probabilistic evaluation research.

Rapid determination of source parameters of the M6.2 Jishishan earthquake in Gansu Province and its application in emergency response

In this study, we swiftly determined the focal parameters (focal mechanism, seismic imaging process, magnitude) of the Jishishan earthquake, leveraging a solved fault model to assess the intensity field and casualties promptly. The investigation began by retrieving the source mechanism through the P-wave initial motion and W-phase method. This enabled us to chart the spatial and temporal distribution of energy release in the source area via the back-projection technique. Following this, we estimated the earthquake's intensity field by merging the source inversion findings with the ground motion prediction equation. This analysis facilitated the evaluation of earthquake casualties, utilizing the theoretical intensity field and a casualty assessment model. Our findings indicate that the fault type is a thrust fault, characterized by a unilateral rupture in the direction of NW, with a rupture length spanning approximately 10–15 ​km and a duration ranging between 8 and 10 ​s. The earthquake's magnitude varied from M 5.9 to M 6.2. The demarcated high-intensity areas, as per our intensity assessment, align closely with the actual survey results. Furthermore, the predicted total casualties and identified critical rescue zones closely match the real-world casualty figures. These insights offer crucial technical support for governmental emergency command and rescue operations.

Multi-stage and multi-objective design optimization for improving resilience of base-isolated hospital buildings

The hospital is a critical infrastructure for treating the injured after the earthquake. The hospital is a complex system composed of structure, non-structural components, and medical equipment that are located on different floors, have different importance and seismic performance, and need different control objectives, enhancing any component could improve the hospital's functionality. It is imperative to economically and rationally choose enhancement measures to minimize initial functionality losses and improve hospital resilience. This study provides a hospital functional evaluation method that takes into account the physical dimension of the damage to the structure, non-structural components and medical equipment, as well as the organizational dimension of the treatment process of earthquake casualties. Bayesian networks and discrete event simulation methods are used to calculate the availability probability of medical rooms and the functional indicator of the number of earthquake casualties that can be treated. Then, a two-stage and multi-objective functionality improvement method was proposed to improve hospital functionality. The first stage is the design optimization of the isolation system with the objectives of minimizing the functionality loss and displacement of the isolation layer. The second stage optimizing the enhancement strategies for non-structural components and medical equipment aims at minimizing the functionality loss and enhancement budget. The proposed method uses a fast and elitist non-dominated sorting genetic algorithm (NSGA-II) approach to optimize the design of the isolation system and the enhancement of non-structural components and medical equipment. The method is applied to a hypothetical hospital to highlight the proposed method's capability of improving the hospital's functionality.

Experimental Evaluation of Post-Earthquake Fire on Reinforced Concrete Structures—A Review

This review aims to investigate the recent advancements in the performance of earthquake-damaged reinforced concrete structures subjected to fire loading and the damages that are induced by fire to reinforced concrete structural elements with damage due to seismic loads. The first part of the paper provides a general understanding of the Post-Earthquake Fire (PEF) effect on reinforced concrete structures, and some statistics regarding the previous earthquake casualties and amounts of destruction in different regions of the world are addressed. In the second part of the investigation, an in-depth review of the experimental and numerical procedures of PEF analysis in concrete columns, beams, slabs, and full-scale RC frames and the types of damages in RC members due to PEF have been presented, giving a general review of the results and conclusions in previous research in PEF in different reinforced concrete structures. In the end is discussed the concept of hybrid fire simulation, its applications in engineering problems, the methodologies that consider full interaction effects as well as recent breakthroughs in studying PEF using Real-Time Hybrid Simulation.

Quick prediction of seismic casualties using fuzzy Case-Based Reasoning technology

Rapid prediction of earthquake casualties is vital to improve the efficiency of emergency rescue and reduce social losses. Using the Delphi process, nine feature attributes affecting post-earthquake casualties are identified. Corresponding membership functions for the feature attributes are established based on fuzzy theory. The objective weights of feature attributes obtained from the entropy technology are applied to modify the subjective weights from Analytical Hierarchy Process (AHP). To expand the size of the case base, a new idea of collecting cases based on seismic intensity scenarios is proposed. A numerical experiment is carried out for all cases in the case base along the proposed fuzzy Case-Based Reasoning technical route. The average prediction error is only 14.93%.

State of the Art of Seismic Protection Technologies for Non-Engineered Buildings (N-EBs) in Developing Regions of the World

ABSTRACT On a global scale, most earthquake casualties are the result of non-engineered buildings (N-EBs) collapsing in developing regions of the world. This is because developing countries present the unfortunate combination of high population densities, lack of resources, corruption, and a lack of design and construction knowhow needed in seismic regions. Over the last decades, a significant research effort has been dedicated to introducing low-cost seismic protection strategies that could be implemented on a large scale in developing regions of the world. This paper presents those state-of-the-art technologies. The purpose of this paper is to provide an overview of the many available systems, and underline the characteristics of these technology, to expand the range of applicability of each system, and to contribute to its state of development.

A Bayesian Network Model for Rough Estimations of Casualties by Strong Earthquakes in Emergency Mode

<p>Rough estimations in emergency mode are now playing an important role in making key decisions for managing disasters including search and rescue. Most of the studies only paid attention to the earthquakes and ignored the presence of disaster chains and the hazard interactions in earthquakes. Bayesian Networks are ideal tools to explore the causal relationships between events, combine prior knowledge and observed data, and are integrated to solve uncertain problems. In such situations, we present improvements based on a Bayesian Network Model in approaches to estimations of casualties in earthquakes. According to the development of the earthquake disaster chain in literature, the proposed model extracts the key events of earthquakes, considers the hazard interactions, and constructs the Bayesian Networks based on a scenario-based method, to deal with the events in the earthquakes. In the model, lifeline system damages, fires, landslides, and debris flow have been integrated into the networks. The conditional probability tables are encoded by using the collected cases. Validations in the Netica allow the simulation of expected shaking intensity and estimation of the expected casualties by strong earthquakes in emergency mode. Compared to the literature, the method is closer to the fact in the rough estimations, providing important information for our response to earthquakes. Further, rough estimations are started when only seismic intensity or fewer earthquake source parameters are available.</p> <p> </p>

Postearthquake Casualty Prediction Based on Heatmaps and Wavelet Supporting Vector Machines

The prediction of casualties in earthquakes is very important for improving the efficiency of emergency rescue measures and reducing the number of casualties. Given the time lag and poor accuracy of population density data published in statistical yearbooks, a Baidu heatmap is used in this study to accurately estimate the regional population density. Based on the standard support vector machine (SVM) prediction model, a piecewise loss function and a robust wavelet kernel function are proposed to effectively reduce the prediction error. Given a characteristic attribute set of factors related to earthquake casualties, the new prediction model is tested in 34 cases involving earthquake cases on the Chinese mainland since 2011. Compared with other prediction techniques, the proposed robust wavelet SVM can converge more quickly, and the prediction error is lower than that of the standard backpropagation neural network (BPNN) and standard SVM.

An automated machine learning approach for earthquake casualty rate and economic loss prediction

This study presents an automated machine learning (AutoML) framework to predict the casualty rate and direct economic loss induced by earthquakes. The AutoML framework enables automated combined algorithm selection and hyperparameter tuning (CASH), reducing the manual works in the model development. The proposed AutoML framework includes 5 modules: data collection, data preprocessing, CASH, loss prediction, and model analysis. The AutoML models are learned from the dataset that is composed of earthquake information and social indicators. The optimal algorithm and hyperparameter setting of models are determined by the CASH module. A two-step model including a classifier and a regression model is designed for the casualty rate to address zero-casualty cases and also minimize their impacts on data distribution. The proposed AutoML framework is implemented on the seismic loss dataset of mainland China to demonstrate its practicability. A comparison study is conducted to show the high predictive abilities of the AutoML model compared with the traditional seismic risk model and other AutoML models. Models learned from the complete dataset achieve the ultimate performance compared with subsets that are composed of partial features. The model interpretation results indicate that earthquake magnitude, position, and population density are leading indicators for loss prediction.

A Zoning Earthquake Casualty Prediction Model Based on Machine Learning

The evaluation of mortality in earthquake-stricken areas is vital for the emergency response during rescue operations. Hence, an effective and universal approach for accurately predicting the number of casualties due to an earthquake is needed. To obtain a precise casualty prediction method that can be applied to regions with different geographical environments, a spatial division method based on regional differences and a zoning casualty prediction method based on support vector regression (SVR) are proposed in this study. This study comprises three parts: (1) evaluating the importance of influential features on seismic fatality based on random forest to select indicators for the prediction model; (2) dividing the study area into different grades of risk zones with a strata fault line dataset and WorldPop population dataset; and (3) developing a zoning support vector regression model (Z-SVR) with optimal parameters that is suitable for different risk areas. We selected 30 historical earthquakes that occurred in China’s mainland from 1950 to 2017 to examine the prediction performance of Z-SVR and compared its performance with those of other widely used machine learning methods. The results show that Z-SVR outperformed the other machine learning methods and can further enhance the accuracy of casualty prediction.

Assessment and determination of earthquake casualty gathering area based on building damage state and spatial characteristics analysis

Timely determination of the spatial distribution of post-earthquake relief forces and appropriate levels of response is a daunting task. This paper proposes a method based on building damage state and spatial characteristics analysis to assess the hotspot distribution of earthquake casualties and determine the dangerous gathering area. The method comprises three steps: (1) use the basic information of the buildings in the study area to calculate the building damage state and build the relationship with the casualties; (2) mesh the research area and establish the mapping relationship with the casualties caused by the building damage; (3) use grid data for spatial autocorrelation analysis (Moran's I) and hotspot analysis (Getis-Ord Gi*) to comprehensively assess the casualty cluster area. The feasibility of the proposed method was validated using the seismic damages of Longtoushan Town in the 2014 Ludian earthquake of China. The damage state of buildings and the number of casualties evaluated by this method coincide quite well with the actual earthquake results, and the casualty concentration area is determined. This method quantifies the degree of spatial risk aggregation of casualties and provides scientific guidance for disaster prevention planning and rescue.

Assessment of earthquake casualties and comparison of accuracy of five injury triage methods: evidence from a retrospective study

ObjectiveThe use of an injury triage method among earthquake injury patients can facilitate the reasonable allocation of resources, but the various existing injury triage methods need further confirmation. This study...

A new model for the quantitative assessment of earthquake casualties based on the correction of anti-lethal level

A new model for the quantitative assessment of earthquake casualties based on the correction of anti-lethal level

Fragility estimation for global building classes using analysis of the Cambridge earthquake damage database (CEQID)

This paper describes CEQID, a database of earthquake damage and casualty data assembled since the 1980s based on post-earthquake damage surveys conducted by a range of research groups. Following 2017–2019 updates, the database contains damage data for more than five million individual buildings in over 1000 survey locations following 79 severely damaging earthquakes worldwide. The building damage data for five broadly defined masonry and reinforced concrete building classes has been assembled and a uniform set of six damage levels assigned. Using estimated peak ground acceleration (PGA) for each survey location based on USGS Shakemap data, a set of lognormal fragility curves has been developed to estimate the probability of exceedance of each damage level for each class, and separate fragility curves for each of five geographical regions are presented. A revised set of fragility curves has also been prepared in which the bias in the curve resulting from the uncertainty in the ground motion parameter has been removed. The uncertainty in the fragility curves is evaluated and discussed and the curves are compared with those from other studies. A resistance index for each class of building is developed and cross-regional comparisons using this resistance index are presented.

REVIEW OF ESTIMATION METHODS AND SYSTEMS USED TO PREDICT EARTHQUAKE CASUALTIES

Earthquake casualty estimation is key to earthquake resistance and hazard prevention planning prior to earthquakes and emergency rescue efforts after earthquakes. Scholars in the fields of earthquake engineering, geophysics, sociology, public health etc. have performed multi-perspective studies on this issue. Earthquake casualty assessment models are divided into global/regional models and local models. According to their relevant factors, they are categorized based on the appropriate seismic parameters and building fragility factors, and some researchers additionally consider the influence of secondary disasters and population characteristics. Following an introduction to the importance of earthquake casualty assessment work, traditional casualty estimation methods, and emerging methods combined with artificial intelligence, big data and spatial information technology are summarized in this review. The framework, technical foundation and application of earthquake loss estimation systems are introduced, and future development trends are envisioned.

Lessons from the casualties caused by the Changning Ms 6.0 earthquake in China

This paper summarizes the Ms 6.0 earthquake disaster that occurred in Changning, Sichuan Province, China, on 17 June 2019. Additionally, a description of the disaster emergency response on both governmental and non-governmental levels is provided. As determined by on-site investigation and analysis, the direct causes of the earthquake casualties are summarized. Among them, 66% of the casualties can be attributed to the lack of anti-seismic measures, 25% to improperly avoiding danger during the earthquake, 6% to earthquake-induced geological disasters, and 3% to pre-existing diseases exacerbated by the earthquake. Responding to the causes of these four casualty types, we summarize 4 lessons: 1) build housing in accordance with earthquake-resistant requirements, 2) propagandize common sense about avoiding danger during earthquakes to public, 3) investigate and evaluate potential geological disasters in the stricken area and try to prevent and mitigate them, and 4) establish cooperation with health organizations, focusing on people who are older or have serious illnesses and give them help.

Instant prediction of earthquake casualties for early rescue planning: A joint Poisson mixed modeling approach

Reasonable and timely prediction of the number of earthquake casualties is crucial to reduce the risk of severe earthquake casualties. Good real-time predictions help government decision makers make informed emergency rescue decision in a timely manner. To provide timely predictions of casualties for an early rescue planning, we only consider factors available to government agencies early on. These factors include earthquake magnitude, aftershocks, population density, depth of epicenter, time of earthquake and location of epicenter. Casualties caused by earthquakes of similar magnitude often vary greatly from region to region; however, the types of death, serious injury and minor injury, associated with each earthquake are highly correlated. Therefore, we consider a joint Poisson mixed model for death, serious injury and minor injury, in which earthquake-specific random effects can help capture the uncertainty between earthquakes and characterize the positive correlation between the three types of casualties. Furthermore, we have applied this method to analyze the casualty data in two very different regions. The first one is Yunnan province which is a region of frequent earthquakes in China, and the other is Indonesia, a country with frequent earthquakes. In both cases, our method provided good estimates for the numbers of death, serious injury and minor injury simultaneously. In addition, the predicted random effects associated with our method not only help identify anomalous earthquake casualties, but also provide a basis for probabilistic prediction of severe casualties.

A stacking-based ensemble learning method for earthquake casualty prediction

The estimation of the loss and prediction of the casualties in earthquake-stricken areas are vital for making rapid and accurate decisions during rescue efforts. The number of casualties is determined by various factors, necessitating a comprehensive system for earthquake-casualty prediction. To obtain accurate prediction results, an effective prediction method based on stacking ensemble learning and improved swarm intelligence algorithm is proposed in this study, which comprises three parts: (1) applying multiple base learners for training, (2) using a stacking strategy to integrate the results generated by multiple base learners to obtain the final prediction results, and (3) developing an improved swarm intelligence algorithm to optimize the key parameters in the prediction model. To verify the effectiveness of the model, we collected data pertaining to earthquake destruction from 1966 to 2017 in China. Experiments were conducted to compare the proposed method with popular machine learning methods. It was found that the stacking ensemble learning method can effectively integrate the prediction results of the base learner to improve the performance of the model, and the improved swarm intelligence algorithm can further improve the prediction accuracy. Moreover, the importance of each feature was evaluated, which has important implications for future work such as casualty prevention and rescue during earthquakes.

Application of improved ELM algorithm in the prediction of earthquake casualties.

Earthquake casualties prediction is a basic work of the emergency response. Traditional forecasting methods have strict requirements on sample data and lots of parameters are required to be set manually, which can result in poor results with low prediction accuracy and slow learning speed. In this paper, the Extreme Leaning Machine (ELM) is introduced into the earthquake disaster casualty predictions with the purpose of improving the prediction accuracy. However, traditional ELM model still has the problems of poor network structure stability and low prediction accuracy. So an Adaptive Chaos Particle Swarm Optimization (ACPSO) is proposed to the optimize traditional ELM's network parameters to enhance network stability and prediction accuracy, and the improved ELM model is applied to earthquake disaster casualty prediction. The experimental results show that the earthquake disaster casualty prediction model based on ACPSO-ELM algorithm has better stability and prediction accuracy. ACPSO-ELM algorithm has better practicality and generalization in earthquake disaster casualty prediction.

The casualty prediction of earthquake disaster based on Extreme Learning Machine method

In the prediction of casualties of earthquake disaster, the traditional prediction method requires strict sample data, and it is necessary to manually set a large number of parameters, resulting in poor prediction accuracy and slow learning speed. This paper introduces the Extreme Learning Machine (ELM) into the earthquake casualty prediction, aiming to improve the prediction accuracy. Through the data training, the ELM network structure of earthquake victims’ casualty prediction is established, and the number of hidden layer nodes and the excitation function are determined, which ensures the reliability of the ELM network prediction results. Based on the data of 84 groups of earthquake victims from China in 1970–2017, the ELM algorithm, BP neural network, SVM and modified partial Gaussian curve were compared and verified. The results show that the average relative error of ELM algorithm for earthquake disaster prediction is 3.37%, the coefficient of determination R-square is 0.96, the average relative error of injury prediction is 1.04%, and the coefficient of determination R-square is 0.97, which indicates that the ELM algorithm has good robustness and generalization ability.