Observations on the application of artificial neural network to predicting ground motion measures

作     者：Hanping Hong Taojun Liu Chien-Shen Lee 

作者机构：Department of Civil and Environmental Engineering University of Western Ontario London Ontario N6A 5B9 Canada 

出 版 物：《Earthquake Science》 (地震学报（英文版）)

年 卷 期：2012年第25卷第2期

页      面：161-175页

核心收录：

学科分类：0709[理学-地质学] 0819[工学-矿业工程] 07[理学] 070801[理学-固体地球物理学] 0707[理学-海洋科学] 0818[工学-地质资源与地质工程] 0708[理学-地球物理学] 0815[工学-水利工程] 0816[工学-测绘科学与技术] 0813[工学-建筑学] 0825[工学-航空宇航科学与技术] 0704[理学-天文学] 0814[工学-土木工程] 

基　　金：The financial support received from the Natural Science and Engineering Research Council of Canada the University of Western Ontario 

主　　题：neural network peak ground acceleration pseudospectral acceleration seismic ground motion measures uncertainty 

摘      要：Application of the artificial neural network （ANN） to predict pseudospectral acceleration or peak ground acceleration is explored in the study. The training of ANN model is carried out using feed-forward backpropagation method and about 600 records from 39 California earthquakes. The statistics of the residuals or modeling error for the trained ANN-based models are almost the same as those for the parametric ground motion prediction equations, derived through regression analysis; the residual or modeling error can be modeled as a normal variate. The similarity and differences between the predictions by these two approaches are shown. The trained ANN-based models, however, are not robust because the models with almost identical mean square errors do not always lead to the same predictions. This undesirable behaviour for predicting the ground motion measures has not been shown or discussed in the literature; the presented results, at least, serve to raise questions and caution on this problem. A practical approach to ameliorate this problem, perhaps, is to consider several trained ANN models, and to take the average of the predicted values from the trained ANN models as the predicted ground motion measure.