[1]ZHOU Yu,LUO Huan.Eigenvectors-informed Support Vector Machines for Fragility Curve Predictions of RC Frames[J].Journal of Seismological Research,2024,47(03):359-368.[doi:10.20015/j.cnki.ISSN1000-0666.2024.0052
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Eigenvectors-informed Support Vector Machines for Fragility Curve Predictions of RC Frames
Journal of Seismological Research[ISSN 1000-0666/CN 53-1062/P] Volume:
47
Number of periods:
2024 03
Page number:
359-368
Column:
人工智能
Public date:
2024-05-01
- Title:
- Eigenvectors-informed Support Vector Machines for Fragility Curve Predictions of RC Frames
- Keywords:
- RC frame structures; fragility curves; eigenvectors; support vector machines; machine learning
- CLC:
- TU973.2
- DOI:
- 10.20015/j.cnki.ISSN1000-0666.2024.0052
- Abstract:
- Fragility curves establish a correlation between structural damage levels and seismic intensity,offering an intuitive depiction of the probability of structural failure. However,the generation of these curves necessitates a substantial amount of structural nonlinear time-history analysis results,thereby rendering the computational process inefficient. Machine learning techniques have been proven to effectively address this issue,yet their efficacy diminishes with the increase in the scale of training data due to the computational demands of solving large-scale inverse matrices during the training phase. In response,this paper proposes a novel methodology,the Eigenvector Information-supported Support Vector Machine(EILS-SVM),which surmounts the limitations associated with these techniques. By employing a selective subsample to construct a low-rank kernel matrix in the context of large-scale datasets,the EILS-SVM method requires only the inversion of small-scale,low-rank matrices,significantly enhancing computational efficiency. To validate the accuracy and efficiency of the EILS-SVM,it is benchmarked against conventional models such as the Least Squares Support Vector Machine(LS-SVM),Random Forest,Neural Networks,Linear Discriminant Analysis(LDA),and Bayesian methods,using a dataset comprised of 16500 instances of damage in Reinforced Concrete(RC)frames subjected to seismic activities. The results indicate that the EILS-SVM is capable of accurately predicting the fragility curves of RC frames,with a computational efficiency improvement of up to 27 times compared to existing methodologies.
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Last Update:
2024-05-01