2022 年 22 巻 6 号 p. 6_39-6_56
We constructed ground motion evaluation models of peak accelerations and response spectra using supervised machine learning based on a strong motion database. The common logarithmic standard deviations of the ratios of the predicted values to the observed ones in our models were 0.18-0.21; the variation here is less than that in previous ground motion prediction equations. The generalizability of the models was tested on the data of three earthquakes that occurred after the earthquakes used for the training dataset. The results showed that the prediction accuracy decreased for earthquakes with features that were not included in the training dataset; however, the models with features based on prediction results using the previous ground motion prediction equation could compensate for the bias and lack of training data.
