Empirical Vulnerability Research Articles

A Review of Structural Vulnerability Assessment Methods in the Advent of a Tsunami

The interaction between hazard and exposure gives rise to risk. Tsunami risk assessment is derived from both hazard assessment and vulnerability assessment. Exposure can manifest in various aspects, such as social factors (e.g., population), environmental factors (e.g., agricultural areas), or economic factors (e.g., infrastructure). Our focus in this review paper is specifically on economic exposure, with an emphasis on structural vulnerability assessment. The approaches employed in tsunami vulnerability assessment are diverse. In this paper, we categorize them into simplified and in-depth methods, encompassing: (1) Empirical vulnerability functions, (2) Index-based approach, (3) Building tsunami vulnerability model (BTV).

Development of a seismic vulnerability and risk model for typical bridges considering innovative intensity measures

Seismic intensity measures are one of the core parameters for estimating regional bridges’ seismic risk and vulnerability. Using macroscopic seismic intensity indicators to predict and assess the seismic vulnerability of bridges can contribute positively to establishing urban and rural earthquake risk models. However, the developed vulnerability model rarely considers the contribution of instrument intensity to its quantification results and ignores the influence of nondamage bridge samples on the development of bridge risk models and the membership relationship between seismic vulnerability levels, which makes the developed model somewhat localized and uncertain. This study proposes a quantitative method and innovative model considering composite seismic intensity (instrumental and macroseismic intensity) indicators. The model was updated and validated using 300,000 acceleration records monitored by ten seismic stations during the Luding earthquake on September 5, 2022, in China, and the features of seismic motion parameters were analysed. The established empirical vulnerability dataset of bridges during the 2008 Wenchuan earthquake in China was updated and optimized using the developed models and methods. The traditional vulnerability level of bridges has been expanded by adding and refining actual samples (2317 bridges) from different seismic intensity zones. A vulnerability prediction model based on an updated extended bridge database was developed using nonlinear regression methods. A fragility membership index calculation model for estimating the correlation features of bridge vulnerability levels is proposed by combining the probability risk and fuzzy membership correlation algorithm. The traditional method for calculating the seismic vulnerability index has been improved, and a multiparameter bridge vulnerability model validation and rationality analysis have been conducted using the newly developed bridge sample dataset.

Developing a probability-based technique to improve the measurement of landslide vulnerability on regional roads

Considering the uncertainty in vulnerability curves, this study employs a probabilistic technique to improve the current landslide vulnerability on roads. The uncertainty in current vulnerability was first elaborated, and slope failure and the damage caused to roads were investigated. To minimize uncertainty, subsequently, the probabilistic landslide intensity was proposed, and hired to calibrate the empirical landslide intensity. Finally, an improved vulnerability curve was developed and compared with the empirical vulnerability, in terms of the fluctuation band and residual value. Results show that the uncertainty of the improved vulnerability is significantly reduced compared with the empirical vulnerability model. In detail, the uncertainty reduction in landslide vulnerability is reflected not only in the decrease of the residual range, from 0.6 to 0.3, but in the narrowing of the fluctuation band. It provides a route for enhancing the accuracy of landslide vulnerability from a probabilistic view.

Scenario-Based Seismic Risk Assessment for the Reykjavik Capital Area

About two-thirds of the population in Iceland lives in the Reykjavik capital area (RCA), which is close to active volcanoes and seismic zones. In the period 1900–2019, a total of 53 earthquakes of Mw≥5.0 struck in these zones. The two largest events in the Reykjanes Peninsula, Mw6.36 and Mw6.12, occurred in 1929 and 1968, respectively. Both events were less than 20 km from the outskirts of the RCA. Late in the year 2020, the seismicity on the peninsula greatly increased due to magma intrusion and volcanic activity, which has so far resulted in three eruptions, in 2021, 2022, and 2023, and six earthquakes of Mw≥5.0. Based on historical and geological data, the ongoing activity is probably the initial phase of an active period ahead that could continue for many decades, and has the potential to trigger larger earthquakes like those in 1929 and 1968. Further east, in the South Iceland Seismic Zone, two earthquakes of Mw6.52 and 6.44 struck in June 2000, and in May 2008, a Mw6.31 earthquake occurred. In both cases, around 5000 buildings were affected. Insurance loss data from these events have been used to develop empirical vulnerability models for low-rise buildings. In this study, the loss data are used to calibrate seismic vulnerability models in terms of the source-site distance. For a given magnitude scenario, this provides a simpler representation of seismic vulnerability and is useful for emergency planning and disaster management. These models are also used to compute different types of scenario risk maps for the RCA for a repeat of the 1929 earthquake.

Updated empirical vulnerability model considering the seismic damage of typical structures

Updated empirical vulnerability model considering the seismic damage of typical structures

Vulnerability of Older Adults in the Context of Food Purchase

Despite the aging population, studies on the subject are scarce, notably focusing on the impotence experienced by older adults when purchasing food products. This research aims to analyze the vulnerability of older people during their food purchases. Amid the COVID-19 pandemic, we conducted 17 telephone semi-structured interviews with people aged 60 or over, food buyers in person, identified as independent. The content analysis using the software IRAMUTEQ showed problematic packaging and labels; predisposition to repeat previous choices due to the wide variety of foods; excessive portions incompatible with the social changes experienced by older adults; reduction of commensality; physical elements of the supermarket excluding older adults; and the COVID-19 pandemic as an impediment to maximizing well-being due to changes in habits and alternative shopping modalities needs. As a contribution, we propose an empirical vulnerability model that considers the particularities of older people for the food market and other markets.

EMPIRICAL VULNERABILITY ANALYSIS OF RAILWAY BRIDGE SEISMIC DAMAGE BASED ON 2022 MENYUAN EARTHQUAKE

A 6.9 magnitude earthquake at a depth of 10 km struck Menyuan County, Haibei Prefecture, Qinghai Province, China, on January 8, 2022. This earthquake damaged some railway bridges on the Lanzhou-Xinjiang Passenger Dedicated Line. This study combines relevant historical earthquake damage experience, considers the effects of earthquake intensity, site soil classification, superstructure type, foundation failure factor, number of spans, and total bridge length, and develops empirical formulas for seismic damage prediction of railway bridges using ordinal logistic regression model in SPSS software. The seismic damage matrix, as were the anticipated multi-intensity mean damage index and the empirical vulnerability curve based on the two-parameter lognormal distribution function, were generated on this basis. According to the conclusions, although the suggested particular equations and vulnerability curves do not apply to the remainder of the region owing to geographical uniqueness, the technical approach is valid. It may be used as a reference for seismic damage prediction and vulnerability evaluation in other regions. The empirical vulnerability analysis based on the earthquake damage prediction matrix derived from the regression analysis can provide reasonable and fast forecasts before the next earthquake.

Post-earthquake seismic assessment of residential buildings following Sarpol-e Zahab (Iran) earthquake (Mw7.3) – Part 2: Seismic vulnerability curves using quantitative damage index

One of the most crucial steps in disaster management and decision-making is a quick and reliable seismic evaluation of structures following an earthquake. Vulnerability curves, which relate the earthquake intensity measure to damage states, are key terms in performing post-earthquake assessments. In most previous research, empirical vulnerability curves were roughly developed using qualitative criteria. Current research proposes a novel damage index based on the suggested damage states resulting from field investigation of more than 81 damaged steel and RC buildings after the Sarpol-e Zahab (Iran) earthquake. The proposed damage index provides relatively precise information about damaged buildings. Then, using the spectral acceleration derived from the conditional ground motion intensity and the proposed damage index, empirical vulnerability curves are generated for RC and steel buildings and non-structural walls. The developed empirical vulnerability curves are valuable due to the lack of similar field studies on damaged buildings under severe earthquakes and are consistent with common practice of construction in Iran. Past earthquakes' data can be used in seismic risk assessment and planning for mitigation of losses in the future.

Post-earthquake seismic assessment of residential buildings following Sarpol-e Zahab (Iran) earthquake (Mw7.3) part 1: Damage types and damage states

Following a disaster, a quick and reliable evaluation of structural damage and its classification is one of the crucial steps to making decisions and disaster management. Based on a reliable evaluation of structural damage and proper classification of buildings, decisions related to structural performance (functionality), repair possibility, or in severe cases, a replacement could be made. The results of current research are obtained from a comprehensive and meticulous investigation of more than 81 damaged steel and RC buildings after the Sarpol-e Zahab (Iran) earthquake. First, a detailed explanation of various types of structural and non-structural damage to buildings is provided. Then, based on the severity, extent, and types of damage and the observed residual drift for steel structures, buildings are classified into five damage states. The damage types are the more frequently observed damage in different elements. The inherent quantitative classification of buildings into five damage states makes this approach a precise and useful tool to investigate buildings more reliably following earthquakes. Part-1 of this research (current paper) discusses various types of observed damage and the classification of buildings into damage states. In part-2 of this research (accompanying paper), a damage index is proposed based on the results of part-1 and empirical vulnerability curves for different types of damaged buildings are developed.

Rapid Assessment of Seismic Risk for Railway Bridges Based on Machine Learning

When an earthquake occurs, railway bridges will suffer from different degrees of seismic damage, and it is necessary to assess the seismic risk of bridges. Unfortunately, the majority of studies were done on highway bridges without taking into account railway bridge characteristics; hence they are not applicable to railway bridges. Furthermore, current research methods for risk assessment cannot be performed quickly, and suffer from the problems of subjective personal experience, complicated calculations, and time-consuming. This paper we use machine learning for earthquake damage prediction and empirical vulnerability curves to represent risk assessment results, creating a rapid risk assessment procedure. We gathered and tallied seismic damage data from 335 railway bridges that were damaged in the Tangshan and Menyuan earthquakes, found six variables that had a substantial impact on seismic risk outcomes, and categorized the damage levels into five categories. It is essentially a multi-classification and prediction problem. In order to solve this problem, four algorithms were tested: Random Forest (RF) Back Propagation Artiifcial Neural Network (BP-ANN), PSO-Support Vector Machine (PSO-SVM), and K Nearest Neighbor (KNN). It was found that RF is the most effective method, with an accuracy rate of up to 93.31% for the training set and 89.39% for the test set. Then this study describes the new procedure in detail for rapidly assessing seismic risk to 269 bridges chosen at random from the sample pool. Firstly, the seismic damage data of bridges are collated, then the seismic damage rating is predicted using RF, and finally the empirical vulnerability curve is drawn using a two-parameter normal distribution function for the purpose of seismic damage risk assessment. The study’s findings can be used as a guide for choosing a machine learning approach and its inputs to build a rapid assessment model for railway bridges.

A Simplified Prediction Model of Structural Seismic Vulnerability Considering a Multivariate Fuzzy Membership Algorithm

ABSTRACT Fuzzy decision-making and analytic hierarchical processes are ubiquitously used to predict the seismic damage and vulnerability of building clusters. However, the factors affecting the seismic vulnerability of building structures are diversification, cognitive uncertainty, and complex fuzziness. To probe the impact of multiple fuzzy influencing factors on the vulnerability of regional buildings and the degree of membership between them, the development of a structural vulnerability prediction model based on fuzzy decision-making and a hierarchical system with multiple factors has strong theoretical and practical significance. This study proposes a novel method for rapidly predicting structural vulnerability based on a multivariate fuzzy membership index. Rapid fragility prediction models of fiv4e typical structures considering the multivariate fuzzy membership index are established. A new approach is proposed based on the relationship model between the empirical vulnerability index and seven fuzzy membership parameters. Five types of typical structural vulnerability index rapid prediction innovation models are developed. The new prediction model is compared and verified using the quantitative value of China’s macrointensity standard and the structural seismic loss observation data (98,050 × 104 m2 and 995,000 buildings) of 213 typical earthquakes to establish an empirical vulnerability index belt model. The proposed prediction model comprehensively considers multiple fuzzy membership parameters and the empirical structural earthquake damage database. The model analysis and validation results indicate that the proposed model can be further used for the seismic damage evaluation of typical structures and rapid fragility prediction.

Empirical vulnerability estimation models considering updating the structural earthquake damage database

The seismic vulnerability of regional buildings is a fundamental index to effectively predict and estimate the seismic resilience of urban and rural areas. The structural empirical vulnerability database can be used to develop and generate relatively accurate multidimensional vulnerability models. With the development of instrument intensity and the updating of macroseismic intensity scales, the structural vulnerability assessment model established using the traditional regional structural empirical vulnerability database shows features of low accuracy. Therefore, using innovative methods to update the conventional vulnerability estimation model has become a fundamental scientific problem to be urgently solved. In this study, the mixed effects of instrument intensity and macrointensity and their evaluation scale were sufficiently considered, and the traditional vulnerability level quantification scale was improved. The structural empirical vulnerability database of three typical earthquakes in China (Wenchuan in 2008, Yushu in 2010, and Jiuzhaigou in 2017) has been comprehensively updated, and the multiscale statistical and computational model has been developed. A nonlinear regression model based on multidimensional vulnerability quantification parameters was proposed. A comparison model for predicting the actual seismic vulnerability of four types of buildings considering typical earthquakes was developed. The traditional vulnerability index algorithm was updated, and the point cloud and strip domain quantitative model based on the updated empirical vulnerability database (27,394 buildings) was established.

Vulnerability and economic loss evaluation model of a typical group structure considering empirical field inspection data

Evaluating the earthquake economic loss of building clusters is an essential index to measure regional seismic risk and resilience. This study developed a rapid financial loss evaluation model based on the empirical vulnerability parameters of the group structure. The quantitative failure parameters used to evaluate the seismic vulnerability of regional group structures are optimized comprehensively, and a loss evaluation model considering the economic vulnerability index is innovatively proposed. The empirical vulnerability database (field survey data of 212 destructive earthquakes in China from 1975 to 2013 (98,916.841 × 104 m2 buildings)) of typical structures is established. An updated model for structural economic loss assessment is proposed. Using the proposed innovation model and probability statistical method, the economic loss evaluation model and database of typical group structures based on the economic vulnerability index are developed. Using the nonlinear Gaussian distribution and the actual cumulative damage quantification method, the economic loss evaluation comparison model and mean value calculation model of five typical buildings considering the empirical group structural vulnerability database are performed. Ultimately, combined with the proposed innovative quantitative model, the economic vulnerability index matrix (database) and point cloud of typical group structures of 212 destructive earthquakes in China are calculated. The proposed model can cooperate with the macroseismic intensity standard to realize the rapid economic loss assessment of typical buildings. The results of this study can contribute to the evaluation of the economic losses of building clusters in multiple macroseismic intensity regions.

Comparison and modelling of building losses in South Iceland caused by different size earthquakes

In June 2000, two earthquakes of Mw6.52 and Mw6.44 occurred with a 4-day interval in the South Iceland seismic zone, and in May 2008, a Mw6.30 earthquake occurred further west in the zone. Their epicentres and fault ruptures were close to small towns and farms, and almost 5000 residential buildings were affected in each of these two seismic events. Despite substantial damage, no residential building collapsed and there were no fatalities. Catastrophe insurance of buildings is mandatory in Iceland. Driven by insurance claims, repair cost was estimated for every affected building, and registered in two independent datasets, called hereafter the 2000 and the 2008 dataset. Two complete loss datasets from different size earthquakes, affecting the same building typologies in the same region is rare to find in the literature. Valuable information can be drawn by comparing the losses caused by these events on the housing stock. In this study an advanced empirical vulnerability model based on zero-inflated beta regression was fitted to five building typologies, based on GEM taxonomy, independently for the 2000 dataset and the 2008 dataset. The peak ground acceleration (PGA) was used as intensity measure. The vulnerability model and the fragility curves are substantially different from these two datasets. For a given PGA, the mean losses based on the 2000 dataset was typically roughly twice that based on the 2008 dataset. Such a large difference in losses for similar building types located in the same area indicates that PGA is not alone an adequate intensity measure to predict losses. The study also showed that high code buildings performed better than low code buildings.

Vulnerability models of brick and wood structures considering empirical seismic damage observations

To further explore the empirical seismic capacity of brick and wood (BW) structures and the vulnerability characteristics of the multiple seismic intensity regions, a vulnerability analysis was conducted based on historical observation data and a field survey of 154 typical destructive earthquakes occurring in 18 provinces (municipalities and autonomous regions) of China from 1990 to 2010. Based on the data collection, classification, and mathematical statistics of the empirical seismic damage of BW structures, a vulnerability probability matrix model based on 11,520.403 × 104 m2 and 48,456 BW structures was obtained and established. Considering the statistical analysis of the empirical data by year, frequency, survey area, and number, empirical vulnerability models (matrix, point cloud, function, and curve) based on the evaluation of the Chinese seismic intensity scale (CSIS) were established by employing the vulnerability probability model, nonlinear regression theory, and numerical analysis method. The failure ratio (FR) and exceedance probability (EP) were employed as vulnerability assessment parameters, and nonlinear vulnerability demand comparison models considering empirical damage of BW in different years were established. In addition, the mean damage index (MDI) parameter matrix model was proposed to evaluate the overall vulnerability of regional BW. Combined with creating the empirical seismic damage investigation sample database, the evaluation and prediction models of BW vulnerability based on MDI were conducted, considering the overall investigation area.

Evaluating targeted heuristics for vulnerability assessment in flood impact model chains

AbstractIn flood risk management, the choice of vulnerability functions has a remarkable impact on the overall uncertainty of modelling flood damage. The spatial transferability of empirical vulnerability functions is limited, leading to the need for computation and validation of region‐specific vulnerability functions. In data‐scarce regions however, this option is not feasible. In contrast, the physical processes of flood impact model chains can be developed in these regions because of the availability of global datasets. Here we evaluated the implementation of a synthetic vulnerability function into a flood impact model. The function bases on expert heuristics on a targeted sample of representative buildings (targeted heuristics). We applied the vulnerability function in a meso‐scale river basin and evaluated the new function by comparing the resulting flood damage with the damage computed by other approaches, (1) an ensemble of vulnerability functions available from the literature, (2) an individual vulnerability function calibrated with region‐specific data, and (3) the vulnerability function used in flood risk management by the Swiss government. The results show that targeted heuristics can be a valuable alternative for developing flood impact models in regions without any data or only few data on flood damage.

Machine-learning based vulnerability analysis of existing buildings

The paper presents a machine-learning based framework, named VULMA (VULnerability analysis using MAchine-learning), for vulnerability analysis of existing buildings. The underlying idea is to provide an indication of the seismic vulnerability by exploiting available photographs, which can be properly processed to provide some input data for empirical vulnerability algorithms. To this scope, a complete processing pipeline has been defined, which consists in four consecutive modules offering different and specific services. The first module, Street VULMA, performs the image gathering starting from the raw data; the second module, Data VULMA, provides a mean for the data labelling and storage; the third module, Bi VULMA, uses the collected data to train several machine-learning models for image classification; the fourth module, In VULMA, performs a ranking of the images, their analysis and consequently assigns the vulnerability index. The proposed procedure has been employed on the existing building portfolio in an extended area of the municipality of Bisceglie, Puglia, Southern Italy, for which all the modules have been tested and, above all, the machine-learning models of Bi VULMA have been trained. After, in order to test the efficiency and the reliability of the proposed tools, the entire procedure has been applied on five case study buildings. The results in terms of vulnerability index have been compared with the manual computations performed by the authors applying the same algorithm. Despite the proposed tool could be improved or modified in some of its modules, the obtained results show a good effectiveness of VULMA, which opens new scenarios in the field of vulnerability assessment procedures and risk mitigation strategies.

Assessing the Vulnerability of Kota Kinabalu Buildings

A gradual increase in moderate and low seismic activity has occurred in Sabah over the course of several years due to the presence of certain moderately active fault lines in the region. Around 300 moderate earthquakes with magnitudes ranging from MW 4.0 to 7.0 have occurred in the last 120 years. The majority of existing buildings in Sabah are wind and gravity loaded. This study proposes a preliminary seismic vulnerability assessment methodology based on empirical and analytical vulnerability method for 250 existing buildings in Kota Kinabalu city. The empirical vulnerability assessment focuses on building evaluation utilizing a standard Rapid Visual Screening (RVS) method and the FEMA 154 guidebook's moderate seismicity assessment form. A field survey was conducted on the buildings ranging in height from low-rise to high-rise. As a result, when subjected to moderate-intensity earthquakes, 60% of the buildings are classed as susceptible and vulnerable to seismic hazard. The current study included the use of nonlinear static analysis to seven different building cases for further investigation. The findings of the analysis demonstrate that the majority of the buildings respond linearly elastical when subjected to peak ground acceleration (PGA) at 0.17g, which indicate that, buildings without seismic design accumulate damage early when subjected to moderate earthquake loadings.

Improved Vulnerability Index Methodology to Quantify Seismic Risk and Loss Assessment in Reinforced Concrete Buildings

ABSTRACT An improved seismic vulnerability index methodology is formulated on the basis of eight modeled parameters that are designed according to earthquake-resistant design concept and derived from the empirical vulnerability index that was initiated by the Group of National Defense against earthquake, named as GNDT Level approach. The results indicated that there is a good correlation between the analytical modeling approach and the observed fragility features during in-situ field investigations. To prove that the proposed methodology is accurate and reliable, the verification of the current methodology is performed through the experimental testing related to school building damages during Ranau Earthquake.

Reliability-based formulation of building vulnerability to debris flow impacts

Physical building vulnerability to debris flows is defined as the potential damage degree of buildings for a given debris flow intensity. In this paper, the physical characteristics of both debris flow intensity and building response are considered. Uncertainties in building capacity and debris flow intensity are explicitly quantified to evaluate the damage probability of a typical reinforced concrete building subjected to debris flow impact. Four damage states with clear failure mechanisms are defined using multi-source information from field observations, numerical simulation, and expert experience. Two series of fragility models are proposed based on practical debris flow impact pressure models. Several debris flow intensity measures are investigated. A better indication can be provided using the intensity measure that represents a specific failure mechanism; e.g., impact force (hv2) for force-dominated failures or overturning moment (h2v2) for moment-dominated failures, where h and v are debris flow depth and velocity, respectively. The corresponding fragility surfaces best express potential building damage. The intensity thresholds in the proposed fragility curves are consistent with those in empirical vulnerability curves. The methodology presented in this paper promotes vulnerability assessment using physics-based modeling, leading to a more reliable evaluation of building damage caused by debris flows.