Socioeconomic Clustering in Seismic Risk Assessment of Urban Housing Stock

2009 ◽  
Vol 25 (3) ◽  
pp. 619-641 ◽  
Author(s):  
J. S.R. Prasad ◽  
Yogendra Singh ◽  
Amir M. Kaynia ◽  
Conrad Lindholm
Keyword(s):  
Risk Assessment ◽  
Seismic Risk ◽  
Model Building ◽  
Housing Stock ◽  
Indian Subcontinent ◽  
Sample Survey ◽  
Economic Losses ◽  
Urban Habitat ◽  
Building Stock ◽  
Seismic Risk Assessment

A seismic risk assessment methodology based on socioeconomic clustering of urban habitat is presented in this paper. In this methodology, the city is divided into different housing clusters based on socioeconomic level of occupants, representing reasonably uniform seismic risk. It makes an efficient utilization of high resolution satellite data and stratified random sample survey to develop the building stock database. Ten different classes of socioeconomic clusters found in Indian cities are defined and 34 model building types (MBTs) prevalent on the Indian subcontinent have been identified and compared with the Medvedev-Sponheuer-Karnik (MSK) scale, European macroseismic scale (EMS), parameterless scale of seismic intensity (PSI), and HAZUS classifications. Lower and upper bound damage probability matrices (DPMs) are estimated, based on the MSK and EMS intensity scales and experience from past earthquakes in India. A case study of Dehradun, a city in the foothills of Himalayas, is presented. The risk estimates using the estimated DPMs have been compared with those obtained using the PSI scale. It has been observed that poorer people are subjected to higher seismic risk, both in terms of casualties and in terms of percent economic losses.

scholarly journals Seismic Risk of Modern City

2019 ◽  
Vol 13 (1) ◽  
pp. 308-318 ◽  
Author(s):  
Vladislav Zaalishvili ◽  
Olga Burdzieva ◽  
Aleksandr Kanukov ◽  
Dmitry Melkov
Keyword(s):  
Risk Assessment ◽  
Seismic Risk ◽  
Seismic Intensity ◽  
Test Area ◽  
Economic Losses ◽  
Soil Conditions ◽  
Building Stock ◽  
Seismic Risk Assessment ◽  
Modern City ◽  
The City

Aim: The goal of the work was to develop and implement a methodology for the expected seismic risk assessment of a modern city on the example of a test area of Vladikavkaz city. Background: The selected area is characterized by a variety of soil conditions typical for the entire territory of the city. At the same time, building stock includes almost all types of buildings that form the urban environment. Objective: Based on the differentiation of soil conditions, the test area was conditionally divided into 6 sites. Further, site effects of every site were estimated (seismic microzonation work was carried out). Expected seismic intensity (MSK-64) of the sites varied within 7-9 points. Each type of building is characterized by a certain vulnerability to a particular level of seismic impact. Method: The work is focused on the implementation of simple and effective statistical concepts of the MSK-64 scale for the development of express seismic risk assessment methodology. Different soils and types of buildings in different combination caused a different level of expected economic losses. Further, on the basis of taking into account the expected damage in the building stock of Kuybyshev Street, the expected social losses were calculated. In this regard, it is of interest to analyze the seismic risk variations along Kuybyshev Street, which is actually a model of the city. Conclusion: The suggested methodology gives a rapid express assessment of seismic risk for decision making on buildings enforcement on a city level. Seismic risk methodology was corrected for new types of buildings (“Vesna” region) and it was shown that the MSK scale is effective but must be also actualized itself.

Seismic Risk Assessment of Masonry Buildings in Istanbul for Effective Risk Mitigation

2010 ◽  
Vol 26 (4) ◽  
pp. 967-982 ◽  
Author(s):  
M. Altug Erberik
Keyword(s):  
Risk Assessment ◽  
Seismic Risk ◽  
Economic Losses ◽  
Assessment Procedure ◽  
Masonry Building ◽  
Masonry Buildings ◽  
Seismic Risk Assessment ◽  
Priority List ◽  
Earthquake Losses ◽  
Short Period

Unreinforced and non-engineered masonry buildings are highly vulnerable to seismic hazard and constitute a significant percentage of earthquake losses, including both casualties and economic losses. This study presents an engineering application on seismic safety assessment of unreinforced masonry (URM) buildings in Istanbul, Turkey, a metropolitan city under very high seismic risk. Nearly 20,000 masonry buildings were examined through a two-stage assessment procedure in order to identify the addresses of those buildings which are under high seismic risk. Furthermore, the obtained database can be employed in the preparation of an earthquake mitigation strategy for the expected major earthquake in Istanbul. In the first-stage evaluation, buildings are examined visually from the street by considering their basic structural parameters and they are ranked within a priority list in terms of the calculated seismic risk. Next, the buildings identified with higher risk are evaluated in the second stage by using a more detailed procedure. The developed procedure is both an optimal and a practical tool in the seismic risk assessment of large masonry building stocks in a short period of time with limited resources.

Global Seismic Risk Assessment: the Wrong, the Right, and the Truth.

2021 ◽  
Author(s):  
Vitor Silva
Keyword(s):  
Risk Assessment ◽  
Seismic Risk ◽  
Current Practice ◽  
New Technologies ◽  
Risk Model ◽  
Economic Losses ◽  
Earthquake Risk ◽  
Loss Assessment ◽  
Risk Models ◽  
Seismic Risk Assessment

<p>The increase in the global population, climate change, growing urbanization and settlement in regions prone to natural hazards are some of the factors contributing to the increase in the economic and human losses due to disasters. Earthquakes represent on average approximately one-fifth of the annual losses, but in some years this proportion can be above 50% (e.g. 2010, 2011). This impact can affect the sustainable development of society, creation of jobs and availability of funds for poverty reduction. Furthermore, business disruption of large corporations can result in negative impacts at global scale. Earthquake risk information can be used to support decision-makers in the distribution of funds for effective risk mitigation. However, open and reliable probabilistic seismic risk models are only available for less than a dozen of countries, which dampers disaster risk management, in particular in the under-developed world. To mitigate this issue, the Global Earthquake Model Foundation and its partners have been supporting regional programmes and bilateral collaborations to develop an open global earthquake risk model. These efforts led to the development of a repository of probabilistic seismic hazard models, a global exposure dataset, and a comprehensive set of fragility and vulnerability functions for the most common building classes. These components were used to estimate relevant earthquake risk metrics, which are now publicly available to the community.</p><p>The development of the global seismic risk model also allowed the identification of several issues that affect the reliability and accuracy of existing risk models. These include the use of outdated exposure information, insufficient consideration of all sources of epistemic and aleatory uncertainty, lack of results regarding indirect human and economic losses, and inability to forecast detailed earthquake risk to the upcoming decades. These challenges may render the results from existing earthquake loss models inadequate for decision-making. It is thus urgent to re-evaluate the current practice in earthquake risk loss assessment, and explore new technologies, knowledge and data that might mitigate some of these issues. A recent resource that can support the improvement of exposure datasets and the forecasting of exposure and risk into the next decades is the Global Human Settlement Layer, a collection of datasets regarding the built-environment between 1974 and 2010. The consideration of this type of information and incorporation of large sources of uncertainty can now be supported by artificial intelligence technology, and in particular open-source machine learning platforms. Such tools are currently being explored to predict earthquake aftershocks, to estimate damage shortly after the occurrence of destructive events, and to perform complex calculations with billions of simulations. These are examples of recent resources that must be exploited for the benefit of improving existing risk models, and consequently enhance the likelihood that risk reduction measures will be efficient.</p><p>This study presents the current practice in global seismic risk assessment with all of its limitations, it discusses the areas where improvements are necessary, and presents possible directions for risk assessment in the upcoming years.</p>

Seismic risk assessment for the residential buildings of the major three cities in Colombia: Bogotá, Medellín, and Cali

2020 ◽  
Vol 36 (1_suppl) ◽  
pp. 298-320
Author(s):  
Ana Beatriz Acevedo ◽  
Catalina Yepes-Estrada ◽  
Daniela González ◽  
Vitor Silva ◽  
Miguel Mora ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Seismic Risk ◽  
Residential Buildings ◽  
Residential Building ◽  
Exceedance Probability ◽  
Building Stock ◽  
Seismic Risk Assessment ◽  
Earthquake Scenarios ◽  
Residential Building Stock ◽  
Aggregate Loss

This study presents a seismic risk assessment and a set of earthquake scenarios for the residential building stock of the three largest metropolitan centers of Colombia: Bogotá, Medellín and Cali (with 8.0, 2.5, and 2.4 million inhabitants, respectively). A uniform methodology was followed for the development of the seismic hazard, vulnerability, and exposure models, thus allowing a direct comparison between the seismic risk of the different cities. Risk metrics such as exceedance probability curves and average annual losses were computed for each city. The earthquake scenarios were selected considering events whose direct economic impact is similar to the aggregated loss for a probability of exceedance of 10% in 50 years. Results show a higher mean aggregate loss ratio for Cali and similar mean aggregate loss ratios for Bogotá and Medellín. All of the models used in this study are openly accessible, enabling risk modelers, engineers, and stakeholders to explore them for disaster risk management.

scholarly journals Residential building stock modelling for mainland China targeted for seismic risk assessment

2021 ◽  
Vol 21 (10) ◽  
pp. 3031-3056
Author(s):  
Danhua Xin ◽  
James Edward Daniell ◽  
Hing-Ho Tsang ◽  
Friedemann Wenzel
Keyword(s):  
Risk Assessment ◽  
High Resolution ◽  
Seismic Risk ◽  
Residential Buildings ◽  
Mainland China ◽  
Residential Building ◽  
Exposure Model ◽  
Building Stock ◽  
Seismic Risk Assessment ◽  
Residential Building Stock

Abstract. To enhance the estimation accuracy of economic loss and casualty in seismic risk assessment, a high-resolution building exposure model is necessary. Previous studies in developing global and regional building exposure models usually use coarse administrative-level (e.g. country or sub-country level) census data as model inputs, which cannot fully reflect the spatial heterogeneity of buildings in large countries like China. To develop a high-resolution residential building stock model for mainland China, this paper uses finer urbanity-level population and building-related statistics extracted from the records in the tabulation of the 2010 population census of the People's Republic of China (hereafter abbreviated as the “2010 census”). In the 2010 census records, for each province, the building-related statistics are categorized into three urbanity levels (urban, township, and rural). To disaggregate these statistics into high-resolution grid level, we need to determine the urbanity attributes of grids within each province. For this purpose, the geo-coded population density profile (with 1 km × 1 km resolution) developed in the 2015 Global Human Settlement Layer (GSHL) project is selected. Then for each province, the grids are assigned with urban, township, or rural attributes according to the population density in the 2015 GHSL profile. Next, the urbanity-level building-related statistics can be disaggregated into grids, and the 2015 GHSL population in each grid is used as the disaggregation weight. Based on the four structure types (steel and reinforced concrete, mixed, brick and wood, other) and five storey classes (1, 2–3, 4–6, 7–9, ≥10) of residential buildings classified in the 2010 census records, we reclassify the residential buildings into 17 building subtypes attached with both structure type and storey class and estimate their unit construction prices. Finally, we develop a geo-coded 1 km × 1 km resolution residential building exposure model for 31 provinces of mainland China. In each 1 km × 1 km grid, the floor areas of the 17 residential building subtypes and their replacement values are estimated. The model performance is evaluated to be satisfactory, and its practicability in seismic risk assessment is also confirmed. Limitations of the proposed model and directions for future improvement are discussed. The whole modelling process presented in this paper is fully reproducible, and all the modelled results are publicly accessible.

An improved model for seismic risk assessment in Portugal

2018 ◽  
Vol 9 (1) ◽  
pp. 70-83 ◽  
Author(s):  
Mário Marques ◽  
Ricardo Monteiro ◽  
Raimundo Delgado
Keyword(s):  
Risk Assessment ◽  
Real Time ◽  
Seismic Risk ◽  
Economic Losses ◽  
Civil Protection ◽  
Seismic Risk Assessment ◽  
Web Based ◽  
Content Type ◽  
Earthquake Loss ◽  
Emergency Scenarios

Purpose Portugal experienced very destructive earthquakes in the past, such as the well-known “Lisbon earthquake” in 1755. With such in mind, accurate estimates of human and economic losses can play a significant role in providing various societal key players with objective information for response strategies. This paper aims to present the contribution of the most recent study in Portugal (PRISE) concerning comprehensive seismic risk assessment, which can be used as good practice and reproduced in different contexts. Design/methodology/approach PRISE (earthquake loss assessment of the Portuguese building stock) covered three main lines of research, corresponding to the three components typically considered in any seismic risk assessment study: the characterization of the seismic hazard, the identification of the exposure to earthquakes and loss potential and the vulnerability of the exposed assets. Each of these components has been fully characterized through the collection of census and local data (exposure), used to carry out nonlinear analysis (hazard and fragility). Findings By involving different research institutions and partners with extensive knowledge and expertise in the earthquake domains, the developed model is capable of producing economic and human earthquake loss estimates in real time (through an innovative Web-based platform) or for specific event scenarios, considering exposed population, residential and industrial buildings. The platform uses open-source tools and hence, it can be reproduced in other countries or contexts. Research limitations/implications Research wise, the hazard, vulnerability and exposure models can still be significantly improved, e.g. by adding critical infrastructure (hospitals, school buildings and bridges) or updating the nonlinear models, for more accurate loss predictions. Practical implications The findings and loss estimates for different earthquake scenarios show that planned interventions are required. Decision-makers and other relevant stakeholders (Civil Protection) can make use of the developed platform to produce specific estimates, to test the effect of different retrofitting interventions or to plan for emergency scenarios. Originality/value A real-time Web-based framework to estimate building damage and economic/human losses because of seismic events has been developed, aiming to provide the Portuguese Civil Protection and other playmakers with a unique platform for planning and preparing for emergency scenarios.

scholarly journals Developing Seismic Risk Prediction Functions for Structures

2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Naveed Ahmad ◽  
Qaisar Ali ◽  
Muhammad Adil ◽  
Akhtar Naeem Khan
Keyword(s):  
Risk Assessment ◽  
Seismic Risk ◽  
Stone Masonry ◽  
Masonry Building ◽  
Fragility Functions ◽  
Building Stock ◽  
Seismic Risk Assessment ◽  
Scenario Earthquakes ◽  
Nonlinear Static ◽  
Prediction Functions

The paper presents the development of a nonlinear static displacement-based methodology for seismic risk assessment and loss estimation of stone masonry building stock of Pakistan. Experimental investigation of one-third scaled model, tested on shake table, is performed in order to obtain lateral strength and drift limits for stone masonry and develop damage scale for performance-based assessment. Prototype buildings are designed respecting the existing building stock and investigated through nonlinear static and dynamic time history analyses. Nonlinear static mechanical models, for both global and local vulnerabilities, are developed for the considered typology which are used to derive analytical structure-dependent fragility functions considering expected sources of uncertainties explicitly in contrary to the conventional procedures. Furthermore, seismic risk assessment is performed for different scenario earthquakes and presented in terms of structure-independent fragility functions to estimate the mean damage ratio, the repair cost as a fraction of replacement cost, and casualties, with the dispersion being quantified, given source-to-site distance and magnitude for an earthquake event. The methodology is tested for seismic risk assessment of the considered typology in recent 2005 Kashmir earthquake, which is reasonably predicted. Future development of the methodology is required with additional experimental tests on rubble stone masonry material in order to increase confidence in future applications.

Global Seismic Risk Assessment: the Wrong, the Right, and the Truth

2020 ◽  
Author(s):  
Vitor Silva
Keyword(s):  
Risk Assessment ◽  
Seismic Risk ◽  
Current Practice ◽  
New Technologies ◽  
Risk Model ◽  
Economic Losses ◽  
Earthquake Risk ◽  
Loss Assessment ◽  
Risk Models ◽  
Seismic Risk Assessment

<p>The increase in the global population, climate change, growing urbanization and settlement in regions prone to natural hazards are some of the factors contributing to the increase in the economic and human losses due to disasters. Earthquakes represent on average approximately one-fifth of the annual losses, but in some years this proportion can be above 50% (e.g. 2010, 2011). This impact can affect the sustainable development of society, creation of jobs and availability of funds for poverty reduction. Furthermore, business disruption of large corporations can result in negative impacts at global scale. Earthquake risk information can be used to support decision-makers in the distribution of funds for effective risk mitigation. However, open and reliable probabilistic seismic risk models are only available for less than a dozen of countries, which dampers disaster risk management, in particular in the under-developed world. To mitigate this issue, the Global Earthquake Model Foundation and its partners have been supporting regional programmes and bilateral collaborations to develop an open global earthquake risk model. These efforts led to the development of a repository of probabilistic seismic hazard models, a global exposure dataset, and a comprehensive set of fragility and vulnerability functions for the most common building classes. These components were used to estimate relevant earthquake risk metrics, which are now publicly available to the community.</p><p>The development of the global seismic risk model also allowed the identification of several issues that affect the reliability and accuracy of existing risk models. These include the use of outdated exposure information, insufficient consideration of all sources of epistemic and aleatory uncertainty, lack of results regarding indirect human and economic losses, and inability to forecast detailed earthquake risk to the upcoming decades. These challenges may render the results from existing earthquake loss models inadequate for decision-making. It is thus urgent to re-evaluate the current practice in earthquake risk loss assessment, and explore new technologies, knowledge and data that might mitigate some of these issues. A recent resource that can support the improvement of exposure datasets and the forecasting of exposure and risk into the next decades is the Global Human Settlement Layer, a collection of datasets regarding the built-environment between 1974 and 2010. The consideration of this type of information and incorporation of large sources of uncertainty can now be supported by artificial intelligence technology, and in particular open-source machine learning platforms. Such tools are currently being explored to predict earthquake aftershocks, to estimate damage shortly after the occurrence of destructive events, and to perform complex calculations with billions of simulations. These are examples of recent resources that must be exploited for the benefit of improving existing risk models, and consequently enhance the likelihood that risk reduction measures will be efficient.</p><p>This study presents the current practice in global seismic risk assessment with all of its limitations, it discusses the areas where improvements are necessary, and presents possible directions for risk assessment in the upcoming years.</p>

scholarly journals Residential building stock modeling for mainland China targeted for seismic risk assessment

2021 ◽  
Author(s):  
Danhua Xin ◽  
James Edward Daniell ◽  
Hing-Ho Tsang ◽  
Friedemann Wenzel
Keyword(s):  
Risk Assessment ◽  
High Resolution ◽  
Seismic Risk ◽  
Residential Buildings ◽  
Mainland China ◽  
Residential Building ◽  
Exposure Model ◽  
Building Stock ◽  
Seismic Risk Assessment ◽  
Residential Building Stock

Abstract. Previous seismic damage reports have shown that the damage and collapse of buildings is the leading cause of fatality and property loss. To enhance the estimation accuracy of economic loss and fatality in seismic risk assessment, a high-resolution building exposure model is important. Previous studies in developing global and regional building exposure models usually use coarse administrative level (e.g., county, or sub-country level) census data as model inputs, which cannot fully reflect the spatial heterogeneity of buildings in large countries like China. To develop a high-resolution residential building stock model for mainland China, this paper uses finer urbanity level population and building-related statistics extracted from the records in Tabulation of the 2010 Population Census of the People’s Republic of China (hereafter abbreviated as the “2010-census”). In the 2010-census records, for each province, the building-related statistics are categorized into three urbanity levels (urban, township, and rural). Statistics of each urbanity level are from areas with a similar development background but belong to different administrative prefectures and counties. Due to privacy protection-related issues, these urbanity level statistics are not geo-coded. Therefore, before disaggregating these statistics into high-resolution grid level, we need to determine the urbanity attributes of grids within each province. For this purpose, the geo-coded population density profile (with 1 km × 1 km resolution) developed in the 2015 Global Human Settlement Layer (GSHL) project is selected to divide the 31 provinces of mainland China into 1 km × 1 km grids. Then for each province, the grids are assigned with urban/township/rural attributes according to the population density in the 2015 GHSL profile. Next for each urbanity of each province, the urbanity level building-related statistics extracted from the 2010-census records can be disaggregated into the 2015 GHSL geo-coded grids, and the 2015 GHSL population in each grid is used as the disaggregation weight. Based on the four structure types (steel/reinforced-concrete, mixed, brick/wood, other) and five storey classes (1, 2–3, 4–6, 7–9, ≥ 10) of residential buildings classified in the 2010-census records, we reclassify the residential buildings into 17 building subtypes attached with both structure type and storey class and estimate their unit construction prices. Finally, we develop a geo-coded 1 km × 1 km resolution residential building exposure model for 31 provinces of mainland China. In each 1 km × 1 km grid, the floor areas of the 17 residential building subtypes and their replacement values are estimated. To evaluate the model performance, comparisons with the wealth capital stock values estimated in previous studies at the administrative prefecture-level and with the residential floor area statistics in the 2010-census at the administrative county/prefecture-level are conducted. The practicability of the modeled results in seismic risk assessment is also checked by estimating the seismic loss of residential buildings in Sichuan Province combined with the intensity map of the 2008 Wenchuan Ms8.0 earthquake and an empirical loss function developed from historical seismic damage information in China. Our estimated seismic loss range is close to that derived from field investigation reports. Limitations of this paper and future improvement directions are discussed. More importantly, the whole modeling process of this paper is fully reproducible, and all the modeled results are publicly accessible. Given that the building stock in China is changing rapidly, the results can be conveniently updated when new datasets are available.

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