scholarly journals Study and Application of Seismic Risk and Exposure Model Based on AI Technologies

2021 ◽  
Author(s):  
Changlong Li ◽  
Zongchao Li ◽  
Hongshan Lyu ◽  
Mengtan Gao
Keyword(s):  
Seismic Risk ◽  
Exposure Model ◽  
Model Based

Probabilistic seismic risk assessment of India

2020 ◽  
Vol 36 (1_suppl) ◽  
pp. 345-371
Author(s):  
Anirudh Rao ◽  
Debashish Dutta ◽  
Pratim Kalita ◽  
Nick Ackerley ◽  
Vitor Silva ◽  
...  
Keyword(s):  
Seismic Hazard ◽  
Seismic Risk ◽  
Risk Model ◽  
Building Design ◽  
Design Guidelines ◽  
Exposure Model ◽  
Economic Losses ◽  
Exceedance Probability ◽  
Earthquake Risk ◽  
Earthquake Insurance

This study presents a comprehensive open probabilistic seismic risk model for India. The proposed model comprises a nationwide residential and non-residential building exposure model, a selection of analytical seismic vulnerability functions tailored for Indian building classes, and the open implementation of an existing probabilistic seismic hazard model for India. The vulnerability of the building exposure is combined with the seismic hazard using the stochastic (Monte Carlo) event-based calculator of the OpenQuake engine to estimate probabilistic seismic risk metrics such as average annual economic losses and the exceedance probability curves at the national, state, district, and subdistrict levels. The risk model and the underlying datasets, along with the risk metrics calculated at different scales, are intended to be used as tools to quantitatively assess the earthquake risk across India and also compare with other countries to develop risk-informed building design guidelines, for more careful land-use planning, to optimize earthquake insurance pricing, and to enhance general earthquake risk awareness and preparedness.

scholarly journals Regional based exposure models to account for local building typologies

2021 ◽  
Author(s):  
G. Tocchi ◽  
M. Polese ◽  
M. Di Ludovico ◽  
A. Prota
Keyword(s):  
Seismic Risk ◽  
Large Scale ◽  
Census Data ◽  
Construction Material ◽  
Regional Scale ◽  
Exposure Model ◽  
Masonry Building ◽  
Building Inventory ◽  
Exposure Models ◽  
Regional Vulnerability

AbstractThe development of building inventory is a fundamental step for the evaluation of the seismic risk at territorial scale. Census data are usually employed for building inventory in large scale application and their use requires suitable rules to assign buildings typologies to vulnerability classes, that is an exposure model specific for the considered vulnerability model. Several exposure models are developed proposing class assignment rules that are calibrated on building typological data available from post-earthquake survey data. However, this approach has the drawback of being based on data from specific geographic areas that have been hit by damaging earthquakes. Indeed, the distribution of building typologies can vary greatly for different areas of a country and the diffusion of one building’s typology rather than another one may depend on the availability of construction material in the area, the evolution of construction techniques and the codes in force at the time of construction. This paper aims to improve the exposure modelling at regional scale, investigating the variability of masonry building typologies distribution. It proposes a methodology to recalibrate the exposure models at regional scale and evaluates the influence of the improved characterization of regional vulnerability on damage and risk assessment. The study shows that the analysis of local building typologies may strongly impact on the evaluation of the seismic risk at territorial scale.

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.

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.

scholarly journals Integrated Seismic Risk Assessment in Nepal

2022 ◽  
Author(s):  
Sanish Bhochhibhoya ◽  
Roisha Maharjan
Keyword(s):  
Risk Assessment ◽  
Social Vulnerability ◽  
Seismic Risk ◽  
Exposure Model ◽  
Earthquake Risk ◽  
Seismic Risk Assessment ◽  
True Nature ◽  
Average Annual Loss ◽  
Physical Risk ◽  
Vulnerability Score

Abstract. As Nepal is at high risk of earthquakes, the district-wide (VDC/Municipality level) study has been performed for vulnerability assessment of seismic-hazard, and the hazard-risk study is incorporated with social conditions as it has become a crucial issue in recent years. There is an interrelationship between hazards, physical risk, and the social characteristics of populations which are significant for policy-makers and individuals. Mapping the spatial variability of average annual loss (seismic risk) and social vulnerability discretely does not reflect the true nature of parameters contributing to the earthquake risk, so when the integrated risk is mapped, such combined spatial distribution becomes more evident. The purpose of this paper is to compute the risk analysis from the exposure model of the country using OpenQuake and then integrate the results with socio-economic parameters. The methodology of seismic-risk assessment and the way of combining the results of the physical risk and socio-economic data to develop an integrated vulnerability score of the regions has been described. This study considers all 75 districts and corresponding VDC/Municipalities using the available census. The combined vulnerability score has been developed and presented by integrating earthquake risk and social vulnerability aspects of the country and represented in form of the map produced using ArcGIS 10. The knowledge and information of the relationship between earthquake hazards and the demographic characteristics of the population in the vulnerable area are imperative to mitigate the local impact of earthquakes. Therefore, we utilize social vulnerability study as part of a comprehensive risk management framework to recuperate and recover from natural disasters.

Exposure model for European seismic risk assessment

2020 ◽  
Vol 36 (1_suppl) ◽  
pp. 252-273 ◽  
Author(s):  
Helen Crowley ◽  
Venetia Despotaki ◽  
Daniela Rodrigues ◽  
Vitor Silva ◽  
Dragos Toma-Danila ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Seismic Risk ◽  
Risk Model ◽  
Exposure Model ◽  
Risk Modeling ◽  
Socioeconomic Indicators ◽  
Commercial Building ◽  
Seismic Risk Assessment ◽  
Ground Acceleration ◽  
Horizon 2020

Building exposure and vulnerability models for seismic risk assessment have been the focus of a number of European projects in recent years, but there has never been a concerted effort among the research community to produce a uniform European risk model. The European Commission’s Horizon 2020 SERA project has a work package that is dedicated to that objective, through the development of an exposure model, an associated set of fragility/vulnerability models, and a database of socioeconomic indicators in order to calculate probabilistic integrated seismic risk at a European scale. This article provides details of the development of the first versions of the European exposure model that describe the distribution of the main residential, industrial and commercial building classes across all countries in Europe, as well as their occupants and replacement costs. The v0.1 of the European exposure model has been integrated within the Global Earthquake Model’s global exposure and risk maps. Preliminary analyses using the model show that almost 35% of the residential population in Europe is exposed to a 475-year return period peak ground acceleration (PGA) hazard of at least 0.1 g, thus highlighting the importance of European seismic risk modeling and mitigation.

Population pharmacokinetic and pharmacodynamic analysis of osimertinib.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e20536-e20536
Author(s):  
Martin Johnson ◽  
Henning Schmidt ◽  
Mikael Sunnaker ◽  
Anthony F Nash ◽  
Suman Nayak ◽  
...  
Keyword(s):  
Exposure Model ◽  
Population Pharmacokinetic ◽  
Baseline Serum ◽  
Model Based ◽  
Exposure Response ◽  
Number Of Patients ◽  
Population Pk ◽  
The Impact ◽  
Egfr Tki ◽  
Model Based Analysis

e20536 Background: Osimertinib is an oral, potent, irreversible, CNS active EGFR-TKI, selective for sensitizing (EGFRm) and T790M resistance mutations, indicated for the treatment of patients with T790M positive advanced non-small cell lung cancer who have progressed on or after EGFR-TKI therapy. Osimertinib pharmacokinetics (PK) were evaluated using a population approach and pharmacodynamic (PD) relationships using appropriate modeling approaches. Methods: To understand the impact of covariates on osimertinib PK, a population PK analysis was performed using data from patients who received osimertinib (20–240 mg) during the AURA studies. Exposure metrics were derived from a PK model and used to assess the exposure-response (safety/efficacy) relationship. Efficacy analysis included patients who were T790M positive (n = 710) and safety analysis included all dosed patients (n = 1088). The impact of covariates on exposure-response was assessed. Models accounting for rare safety events were applied to quantify the association between events and exposure. Results: Population PK analyses supported dose- and time-independent PK of osimertinib with no clinically meaningful covariates identified. Patients in the highest exposure quartile (Q4) had a numerically shorter median progression-free survival (8.3 months [95% CI 6.9, 10.5]) compared with patients in Q1, Q2 and Q3 (all 11.2 months [95% CIs 9.7, 12.7; 8.5, 15.6 and 8.7, 13.7, respectively]). A model-based analysis indicated that this effect is likely due to a larger number of patients in Q4 with poor prognostic features, i.e. worse performance status (WHO 1 or 2) and lower baseline serum albumin compared with Q1, Q2 and Q3, rather than to osimertinib exposure. Model-predicted probability of a relationship between osimertinib exposure and LVEF changes was not evident. Model-based analysis predicted that, compared with the median probability (0.03), the probability of a patient experiencing interstitial lung disease may increase with increasing osimertinib exposure (Q1 probability 0.01 [steady-state AUC 6361 nM*h] vs Q4 0.06 [24460 nM*h]) at the 80 mg dose. Conclusions: Population PK and PK-PD analysis is supportive of 80 mg as an appropriate dose for osimertinib. Clinical trial information: NCT01802632; NCT01802632; NCT02094261; NCT02151981.

Development of multi-hazard exposure models from individual building observations for multi-risk assessment purposes

2020 ◽  
Author(s):  
Simantini Shinde ◽  
Juan Camilo Gomez- Zapata ◽  
Massimiliano Pittore ◽  
Orlando Arroyo ◽  
Yvonne Merino- Peña ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Seismic Risk ◽  
Large Scale ◽  
Residential Building ◽  
Expert Elicitation ◽  
Exposure Model ◽  
Specific Reference ◽  
Exposure Models ◽  
Hazard Exposure ◽  
Administrative Units

<p>The modelling of residential building portfolio exposure model for risk and loss estimations due to natural hazards often do not receive as much attention as other components in the risk chain (e.g. hazard intensity distribution, physical vulnerability). Large-scale (nation or region-wide) exposure models, for instance, are often based on information derived from census and aggregated over geographical administrative units. Moreover, it is customary to employ specific exposure/vulnerability schemas that entail a set of mutually exclusive, collectively exhaustive (MECE) building classes, each associated with a fragility/vulnerability model focusing on the specific reference hazard (e.g. HAZUS).</p> <p>In order to improve the reliability of these models, particularly when the composition of the portfolio is expected to be heterogeneous, individual building observations may be required. This process is relevant in order to constrain and validate the underlying model assumptions. The assignment of  single-hazard building classes within a given schema is usually obtained through expert elicitation (e.g., a skilled surveyor). However, if the very same building has to be classified under another vulnerability schema, either for the same hazard (e.g. EMS98 and HAZUS for seismic risk) or, in a multi-risk context, for a different hazard (e.g. tsunami, lahars), this might require a different expertise and the uncertainty of the resulting models could even increase.</p> <p>We propose an innovative method to decouple the collection of exposure information from the development of exposure models in terms of specific vulnerability classes (schemas). Taking advantage of the methodology suggested by Pittore et al., 2018, individual building attributes are observed in the field for a set of surveyed buildings and described in terms of the GEM v2.0 taxonomy,  a widely used and well-established faceted building taxonomy (Brzev et al., 2013). The assignment of a class is carried out in a post-processing stage and within a fully probabilistic framework by evaluating the level of compatibility between the observed building attributes and the classes available within the considered schema.</p> <p>The proposed methodology has been exemplified in Chile and Peru within the framework of the RIESGOS project. Expert structural engineers from CIGIDEN (Chile) and the Universidad de la Sabana (Colombia) carried out a Rapid Remote Visual Screening Survey using the RRVS web tool (e.g. Haas et al., 2016). In the case of seismic risk we focused on three schemas, namely SARA (a custom schema developed within the GEM-SARA Project in South America), and the well-known EMS-98 and HAZUS. The tsunami-focused schema proposed by Suppasri et al. (2013) has been also implemented.</p> <p>Preliminary results for Gran Valparaiso (Chile) and Metropolitan Lima (Peru) study areas show the potential of the proposed methodology for streamlining the development of multi-hazard exposure models and significantly improving the transparency of the risk assessment procedures and the propagation of related uncertainties. The importance of extending the building taxonomy to encompass multi-hazard attributes is also discussed.</p>

scholarly journals A Multiscale Exposure Model for Seismic Risk Assessment in Central Asia

2014 ◽  
Vol 86 (1) ◽  
pp. 210-222 ◽  
Author(s):  
M. Wieland ◽  
M. Pittore ◽  
S. Parolai ◽  
U. Begaliev ◽  
P. Yasunov ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Central Asia ◽  
Seismic Risk ◽  
Exposure Model ◽  
Seismic Risk Assessment
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