Floor accelerations play an important role in seismic design of non-structural components and equipment supported by structures. Large floor accelerations may cause damage to service and may also result in structural damage or even collapse of the buildings. For precision instruments in high-tech plants, even for small floor accelerations can cause great damage. Properly shutting down the machine before the severe damage caused by strong earthquake is one of the main purposes of this research. The six P-wave parameters, including peak measurement of acceleration, peak measurement of velocity, peak measurement of displacement, effective predominant period, integral of squared velocity, and cumulative absolute velocity are estimated from the first three seconds of a vertical ground acceleration time history. Then, a new predictive algorithm is proposed which utilizes the previous parameters with the floor height and the fundamental period of the structure as the new inputs of the support vector regression model. Representative earthquakes, which were recorded by Structure Strong Earthquake Monitoring System (SSEMS) of Central Weather Bureau (CWB) from 1992 to 2017, are collected to construct the support vector regression model to predict peak floor acceleration (PFA) of each floor. The result shows that the accuracy of predicting PFA located within one-level difference of the seismic intensity scale of Taiwan is up to 96.96%. The proposed system can be integrated into the existing earthquake early warning system (EEWS) to provide a full protection to life and economy.
|Translated title of the contribution||Finite Element Analysis of Multielectrode Solar Photovoltaic Modules Subjected to Mechanical Loads|
|Original language||Chinese (Traditional)|
|Number of pages||9|
|Journal||Journal of the Chinese Institute of Civil and Hydraulic Engineering|
|State||Published - Oct 2020|