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Earthquakes and Structures Volume 18, Number 2, February 2020 , pages 163-170 DOI: https://doi.org/10.12989/eas.2020.18.2.163 |
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Real-time seismic structural response prediction system based on support vector machine |
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Kuang Yi Lin, Tzu Kang Lin and Yo Lin
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| Abstract | ||
| Floor acceleration plays a major role in the seismic design of nonstructural components and equipment supported by structures. Large floor acceleration may cause structural damage to or even collapse of buildings. For precision instruments in high-tech factories, even small floor accelerations can cause considerable damage in this study. Six P-wave parameters, namely the peak measurement of acceleration, peak measurement of velocity, peak measurement of displacement, effective predominant period, integral of squared velocity, and cumulative absolute velocity, were estimated from the first 3 s of a vertical ground acceleration time history. Subsequently, a new predictive algorithm was developed, which utilizes the aforementioned parameters with the floor height and fundamental period of the structure as the new inputs of a support vector regression model. Representative earthquakes, which were recorded by the Structure Strong Earthquake Monitoring System of the Central Weather Bureau in Taiwan from 1992 to 2016, were used to construct the support vector regression model for predicting the peak floor acceleration (PFA) of each floor. The results indicated that the accuracy of the predicted PFA, which was defined as a PFA within a one-level difference from the measured PFA on Taiwan | ||
| Key Words | ||
| Support Vector Machine (SVM); Support Vector Regression (SVR); p-wave features; Peak Floor Acceleration (PFA); earthquake early warning; seismic hazard mitigation; reduced-scale model | ||
| Address | ||
| Kuang Yi Lin, Tzu Kang Lin and Yo Lin: Department of Civil Engineering, National Chiao Tung University, 1001 University road, Hsinchu 300, Taiwan, ROC | ||