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.
X. Yuan et al., "A Neural Network-Based Multivariate Seismic Classifier for Simultaneous Post-Earthquake Fragility Estimation and Damage Classification," Engineering Structures, vol. 255, article no. 113918, Elsevier, Mar 2022.
The definitive version is available at https://doi.org/10.1016/j.engstruct.2022.113918
Civil, Architectural and Environmental Engineering
Keywords and Phrases
Artificial neural networks; Fragility estimation; Intensity measures; Multivariate seismic classifier; Seismic damage classification
International Standard Serial Number (ISSN)
Article - Journal
© 2023 Elsevier, All rights reserved.
15 Mar 2022