On the number of records for structural risk estimation in PBEE
Summary Response‐history nonlinear dynamic analysis is an analytical tool that often sees use in risk‐oriented earthquake engineering applications. In the context of performance‐based earthquake engineering, dynamic analysis serves to obtain a probabilistic description of seismic structural vulnerab...
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Veröffentlicht in: | Earthquake engineering & structural dynamics 2019-04, Vol.48 (5), p.489-506 |
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creator | Baltzopoulos, Georgios Baraschino, Roberto Iervolino, Iunio |
description | Summary
Response‐history nonlinear dynamic analysis is an analytical tool that often sees use in risk‐oriented earthquake engineering applications. In the context of performance‐based earthquake engineering, dynamic analysis serves to obtain a probabilistic description of seismic structural vulnerability. This typically involves subjecting a nonlinear numerical computer model to a set of ground‐motions that represent a sample of possible realizations of base acceleration at the site of interest. The analysis results are then used to calibrate a stochastic model that describes structural response as a function of shaking intensity. The sample size of the ground‐motion record set is nowadays usually governed by computation‐demand constraints, yet it directly affects the uncertainty in estimation of seismic response. The present study uses analytical and numerical means to investigate the record sample size, n, required to achieve quantifiable levels of mean relative estimation error on seismic risk metrics. Regression‐based cloud analysis in the context of Cornell's reliability method and incremental dynamic analysis using various intensity measures were employed to derive a relation of the form
Δ/n, where Δ is a parameter that depends on both the dispersion of structural responses and the shape of the hazard curve at the site. For the cases examined, n can be kept in the 40 to 100 range and achieve 10% mean relative error. The study can contribute to guide engineers towards an informed a‐priori assessment of the number of records needed to achieve a desired value for the coefficient of variation of the estimator of structural seismic risk. |
doi_str_mv | 10.1002/eqe.3145 |
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Response‐history nonlinear dynamic analysis is an analytical tool that often sees use in risk‐oriented earthquake engineering applications. In the context of performance‐based earthquake engineering, dynamic analysis serves to obtain a probabilistic description of seismic structural vulnerability. This typically involves subjecting a nonlinear numerical computer model to a set of ground‐motions that represent a sample of possible realizations of base acceleration at the site of interest. The analysis results are then used to calibrate a stochastic model that describes structural response as a function of shaking intensity. The sample size of the ground‐motion record set is nowadays usually governed by computation‐demand constraints, yet it directly affects the uncertainty in estimation of seismic response. The present study uses analytical and numerical means to investigate the record sample size, n, required to achieve quantifiable levels of mean relative estimation error on seismic risk metrics. Regression‐based cloud analysis in the context of Cornell's reliability method and incremental dynamic analysis using various intensity measures were employed to derive a relation of the form
Δ/n, where Δ is a parameter that depends on both the dispersion of structural responses and the shape of the hazard curve at the site. For the cases examined, n can be kept in the 40 to 100 range and achieve 10% mean relative error. The study can contribute to guide engineers towards an informed a‐priori assessment of the number of records needed to achieve a desired value for the coefficient of variation of the estimator of structural seismic risk.</description><identifier>ISSN: 0098-8847</identifier><identifier>EISSN: 1096-9845</identifier><identifier>DOI: 10.1002/eqe.3145</identifier><language>eng</language><publisher>Bognor Regis: Wiley Subscription Services, Inc</publisher><subject>Coefficient of variation ; Computation ; Dynamic analysis ; Earthquake engineering ; Earthquakes ; Error analysis ; Estimation errors ; fragility function ; ground motion record selection ; Mathematical models ; Nonlinear analysis ; nonlinear dynamic analysis ; Nonlinear dynamics ; Nonlinear response ; Records ; Regression analysis ; Reliability analysis ; Risk ; Seismic activity ; Seismic engineering ; Seismic hazard ; seismic reliability ; Seismic response ; Shaking ; Software reliability ; Statistical analysis ; Stochastic models ; Stochasticity ; Vulnerability</subject><ispartof>Earthquake engineering & structural dynamics, 2019-04, Vol.48 (5), p.489-506</ispartof><rights>2018 John Wiley & Sons, Ltd.</rights><rights>2019 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2935-3fc231bab2737667d18ab591ed0aeb4f25cf1d2faff56fcc4e4d7eef18c9d1393</citedby><cites>FETCH-LOGICAL-c2935-3fc231bab2737667d18ab591ed0aeb4f25cf1d2faff56fcc4e4d7eef18c9d1393</cites><orcidid>0000-0002-0460-6558 ; 0000-0002-4076-2718</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Feqe.3145$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Feqe.3145$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Baltzopoulos, Georgios</creatorcontrib><creatorcontrib>Baraschino, Roberto</creatorcontrib><creatorcontrib>Iervolino, Iunio</creatorcontrib><title>On the number of records for structural risk estimation in PBEE</title><title>Earthquake engineering & structural dynamics</title><description>Summary
Response‐history nonlinear dynamic analysis is an analytical tool that often sees use in risk‐oriented earthquake engineering applications. In the context of performance‐based earthquake engineering, dynamic analysis serves to obtain a probabilistic description of seismic structural vulnerability. This typically involves subjecting a nonlinear numerical computer model to a set of ground‐motions that represent a sample of possible realizations of base acceleration at the site of interest. The analysis results are then used to calibrate a stochastic model that describes structural response as a function of shaking intensity. The sample size of the ground‐motion record set is nowadays usually governed by computation‐demand constraints, yet it directly affects the uncertainty in estimation of seismic response. The present study uses analytical and numerical means to investigate the record sample size, n, required to achieve quantifiable levels of mean relative estimation error on seismic risk metrics. Regression‐based cloud analysis in the context of Cornell's reliability method and incremental dynamic analysis using various intensity measures were employed to derive a relation of the form
Δ/n, where Δ is a parameter that depends on both the dispersion of structural responses and the shape of the hazard curve at the site. For the cases examined, n can be kept in the 40 to 100 range and achieve 10% mean relative error. The study can contribute to guide engineers towards an informed a‐priori assessment of the number of records needed to achieve a desired value for the coefficient of variation of the estimator of structural seismic risk.</description><subject>Coefficient of variation</subject><subject>Computation</subject><subject>Dynamic analysis</subject><subject>Earthquake engineering</subject><subject>Earthquakes</subject><subject>Error analysis</subject><subject>Estimation errors</subject><subject>fragility function</subject><subject>ground motion record selection</subject><subject>Mathematical models</subject><subject>Nonlinear analysis</subject><subject>nonlinear dynamic analysis</subject><subject>Nonlinear dynamics</subject><subject>Nonlinear response</subject><subject>Records</subject><subject>Regression analysis</subject><subject>Reliability analysis</subject><subject>Risk</subject><subject>Seismic activity</subject><subject>Seismic engineering</subject><subject>Seismic hazard</subject><subject>seismic reliability</subject><subject>Seismic response</subject><subject>Shaking</subject><subject>Software reliability</subject><subject>Statistical analysis</subject><subject>Stochastic models</subject><subject>Stochasticity</subject><subject>Vulnerability</subject><issn>0098-8847</issn><issn>1096-9845</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp10EtLAzEUBeAgCo5V8CcE3LiZepPMI1mJlvEBhSroOmQyNzi1nbTJDKX_3ql16-puPu45HEKuGUwZAL_DLU4Fy_ITkjBQRapklp-SBEDJVMqsPCcXMS4BQBRQJuR-0dH-C2k3rGsM1Dsa0PrQROp8oLEPg-2HYFY0tPGbYuzbtelb39G2o2-PVXVJzpxZRbz6uxPy-VR9zF7S-eL5dfYwTy1XIk-Fs1yw2tS8FGVRlA2Tps4VwwYM1pnjuXWs4c44lxfO2gyzpkR0TFrVMKHEhNwc_26C3w5jD730Q-jGSM2ZAia5Aj6q26OywccY0OlNGAuHvWagD_PocR59mGek6ZHu2hXu_3W6eq9-_Q-nsWXL</recordid><startdate>20190425</startdate><enddate>20190425</enddate><creator>Baltzopoulos, Georgios</creator><creator>Baraschino, Roberto</creator><creator>Iervolino, Iunio</creator><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7TG</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0002-0460-6558</orcidid><orcidid>https://orcid.org/0000-0002-4076-2718</orcidid></search><sort><creationdate>20190425</creationdate><title>On the number of records for structural risk estimation in PBEE</title><author>Baltzopoulos, Georgios ; Baraschino, Roberto ; Iervolino, Iunio</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2935-3fc231bab2737667d18ab591ed0aeb4f25cf1d2faff56fcc4e4d7eef18c9d1393</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Coefficient of variation</topic><topic>Computation</topic><topic>Dynamic analysis</topic><topic>Earthquake engineering</topic><topic>Earthquakes</topic><topic>Error analysis</topic><topic>Estimation errors</topic><topic>fragility function</topic><topic>ground motion record selection</topic><topic>Mathematical models</topic><topic>Nonlinear analysis</topic><topic>nonlinear dynamic analysis</topic><topic>Nonlinear dynamics</topic><topic>Nonlinear response</topic><topic>Records</topic><topic>Regression analysis</topic><topic>Reliability analysis</topic><topic>Risk</topic><topic>Seismic activity</topic><topic>Seismic engineering</topic><topic>Seismic hazard</topic><topic>seismic reliability</topic><topic>Seismic response</topic><topic>Shaking</topic><topic>Software reliability</topic><topic>Statistical analysis</topic><topic>Stochastic models</topic><topic>Stochasticity</topic><topic>Vulnerability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Baltzopoulos, Georgios</creatorcontrib><creatorcontrib>Baraschino, Roberto</creatorcontrib><creatorcontrib>Iervolino, Iunio</creatorcontrib><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environment Abstracts</collection><jtitle>Earthquake engineering & structural dynamics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Baltzopoulos, Georgios</au><au>Baraschino, Roberto</au><au>Iervolino, Iunio</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>On the number of records for structural risk estimation in PBEE</atitle><jtitle>Earthquake engineering & structural dynamics</jtitle><date>2019-04-25</date><risdate>2019</risdate><volume>48</volume><issue>5</issue><spage>489</spage><epage>506</epage><pages>489-506</pages><issn>0098-8847</issn><eissn>1096-9845</eissn><abstract>Summary
Response‐history nonlinear dynamic analysis is an analytical tool that often sees use in risk‐oriented earthquake engineering applications. In the context of performance‐based earthquake engineering, dynamic analysis serves to obtain a probabilistic description of seismic structural vulnerability. This typically involves subjecting a nonlinear numerical computer model to a set of ground‐motions that represent a sample of possible realizations of base acceleration at the site of interest. The analysis results are then used to calibrate a stochastic model that describes structural response as a function of shaking intensity. The sample size of the ground‐motion record set is nowadays usually governed by computation‐demand constraints, yet it directly affects the uncertainty in estimation of seismic response. The present study uses analytical and numerical means to investigate the record sample size, n, required to achieve quantifiable levels of mean relative estimation error on seismic risk metrics. Regression‐based cloud analysis in the context of Cornell's reliability method and incremental dynamic analysis using various intensity measures were employed to derive a relation of the form
Δ/n, where Δ is a parameter that depends on both the dispersion of structural responses and the shape of the hazard curve at the site. For the cases examined, n can be kept in the 40 to 100 range and achieve 10% mean relative error. The study can contribute to guide engineers towards an informed a‐priori assessment of the number of records needed to achieve a desired value for the coefficient of variation of the estimator of structural seismic risk.</abstract><cop>Bognor Regis</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1002/eqe.3145</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0002-0460-6558</orcidid><orcidid>https://orcid.org/0000-0002-4076-2718</orcidid></addata></record> |
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subjects | Coefficient of variation Computation Dynamic analysis Earthquake engineering Earthquakes Error analysis Estimation errors fragility function ground motion record selection Mathematical models Nonlinear analysis nonlinear dynamic analysis Nonlinear dynamics Nonlinear response Records Regression analysis Reliability analysis Risk Seismic activity Seismic engineering Seismic hazard seismic reliability Seismic response Shaking Software reliability Statistical analysis Stochastic models Stochasticity Vulnerability |
title | On the number of records for structural risk estimation in PBEE |
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