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.

KEYWORDS:

• Multivariate fuzzy membership parameter
• rapid prediction vulnerability model
• fuzzy membership parameter complementary symmetric matrix
• vulnerability indices
• fuzzy correlation weight model

Acknowledgments

The model validation data of this study were derived from the China Earthquake Administration (CAE) and the Institute of Engineering Mechanics of CAE. The research described in this study was financially supported by the Basic Scientific Research Business Expenses of Provincial Universities in Heilongjiang Province [2022-KYYWF-1056] and a project funded by Heilongjiang Postdoctoral Science Foundation [LBH-Z22294], China.

Disclosure Statement

No potential conflict of interest was reported by the author(s).