Real-time seismic structural response prediction system based on support vector machine

Kuang Yi Lin, Tzu Kang Lin*, Yo Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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's seismic intensity scale, was 96.96%. The proposed system can be integrated into the existing earthquake early warning system to provide complete protection to life and the economy.

Original languageEnglish
Pages (from-to)163-170
Number of pages8
JournalEarthquake and Structures
Issue number2
StatePublished - Feb 2020


  • Support Vector Machine (SVM)
  • Support Vector Regression (SVR)
  • p-wave features
  • Peak Floor Acceleration (PFA)
  • earthquake early warning
  • seismic hazard mitigation
  • reduced-scale model

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