MATEC Web Conf.
Volume 119, 2017The Fifth International Multi-Conference on Engineering and Technology Innovation 2016 (IMETI 2016)
|Number of page(s)||11|
|Published online||04 August 2017|
Estimation of peak floor acceleration based on support vector regression and p-wave features
Department of Civil Engineering, National Chiao Tung University, Hsinchu 300, Taiwan
a Corresponding author : firstname.lastname@example.org
This study proposes a support vector regression (SVR)–based method to evaluate the peak floor acceleration (PFA) in a building subject to a major earthquake. Support vector machine, which constructs a hyperplane in high-dimensional space to solve multivariate problems, is well known for the outstanding performance in classification and regression. Six P-wave parameters obtained from the vertical component of the first three seconds of the acceleration time history of the ground motion, as well as the floor height and the fundamental period of the structure, are adopted as the eight input variables. With these and the support of SVR, the PFA of a specific building can be rapidly estimated to avoid the necessity of installing accelerometers on the building. The SVR model is trained on 2274 representative earthquake records from the Structure Strong Earthquake Monitoring System (SSEMS) of the Central Weather Bureau (CWB) in Taiwan and tested on 757 independent test earthquake records. As demonstrated, the accuracy of the predicted PFA, located within a one-level difference on the seismic intensity scale of Taiwan from the monitored PFA, is 95.51%. The developed algorithm can be integrated into an existing earthquake early warning system (EEWS) to provide comprehensive protection during major earthquakes.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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