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

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...

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Published inJournal of earthquake engineering : JEE Vol. 28; no. 3; pp. 707 - 730
Main Author Li, Si-Qi
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 17.02.2024
Taylor & Francis Ltd
Subjects
Algorithms
Buildings
Cognitive ability
Damage assessment
Decision analysis
Decision making
Decision theory
Earthquake damage
Earthquakes
Empirical analysis
Fragility
fuzzy correlation weight model
fuzzy membership parameter complementary symmetric matrix
Mathematical models
Multivariate analysis
Multivariate fuzzy membership parameter
Parameters
Prediction models
rapid prediction vulnerability model
Regional development
Seismic activity
Seismic hazard
Seismic surveys
Structures
Vulnerability
vulnerability indices
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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 × 10 4 m 2 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.
AbstractList 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 × 10 4 m 2 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.
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.
Author Li, Si-Qi
Author_xml – sequence: 1
  givenname: Si-Qi
  surname: Li
  fullname: Li, Si-Qi
  email: lisiqi@hlju.edu.cn
  organization: Harbin Institute of Technology
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Snippet Fuzzy decision-making and analytic hierarchical processes are ubiquitously used to predict the seismic damage and vulnerability of building clusters. However,...
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SubjectTerms Algorithms
Buildings
Cognitive ability
Damage assessment
Decision analysis
Decision making
Decision theory
Earthquake damage
Earthquakes
Empirical analysis
Fragility
fuzzy correlation weight model
fuzzy membership parameter complementary symmetric matrix
Mathematical models
Multivariate analysis
Multivariate fuzzy membership parameter
Parameters
Prediction models
rapid prediction vulnerability model
Regional development
Seismic activity
Seismic hazard
Seismic surveys
Structures
Vulnerability
vulnerability indices
Title A Simplified Prediction Model of Structural Seismic Vulnerability Considering a Multivariate Fuzzy Membership Algorithm
URI https://www.tandfonline.com/doi/abs/10.1080/13632469.2023.2217945
https://www.proquest.com/docview/2921035334
Volume 28
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