A Neural Network-Based Multivariate Seismic Classifier for Simultaneous Post-Earthquake Fragility Estimation and Damage Classification


A scalar intensity measure (IM) could be insufficient to represent the earthquake intensity and variety in fragility estimation. Introducing multiple IMs to conventional regression of fragility functions can be computationally demanding and require priori assumptions of functional forms. In this study, multivariate seismic classifiers with multiple IMs as inputs are developed based on artificial neural networks (ANNs) to address the above disadvantages of traditional regression approaches. Case studies of a four-story code-conforming benchmark building indicate that fragility estimates from multi-IM ANN classifiers lead to higher accuracy (5.0% to 7.7%) in system-level and element-level damage classification than the single-IM traditional fragility curves. Further studies of IM combinations show that the ANN performance can be improved by more IMs correlated with structural responses while compromised by redundant irrelevant IMs. The optimal IM set should be determined by correlation ranking and ANN predictive performance together. Moreover, the ANN configuration of the case-study building is optimized with five readily available IMs as inputs, which enable a near real-time (within 0.3 ms) prediction of future earthquake damage while maintain high predictive performance. Overall, the multivariate ANN seismic classifier can be a promising tool for simultaneous seismic fragility estimation and damage assessment.


Civil, Architectural and Environmental Engineering


Financial support to complete this study was provided in part by the U.S. Department of Transportation, Office of Assistant Secretary for Research and Technology under the auspices of Mid-America Transportation Center at the University of Nebraska, Lincoln (grant no. 00072738).

Keywords and Phrases

Artificial neural networks; Fragility estimation; Intensity measures; Multivariate seismic classifier; Seismic damage classification

International Standard Serial Number (ISSN)

1873-7323; 0141-0296

Document Type

Article - Journal

Document Version


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© 2022 Elsevier, All rights reserved.

Publication Date

15 Mar 2022