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论文类型：期刊论文

发表时间：2014-01-01

发表刊物：NEURAL NETWORK WORLD

收录刊物：SCIE、EI、Scopus

卷号：24

期号：1

页面范围：89-102

ISSN号：1210-0552

关键字：Vibration; railway; train; building; support vector machine; shuffled frog-leaping algorithm

摘要：Train-induced vibration prediction in multi-story buildings can effectively provide the effect of vibrations on buildings. With the results of prediction, the corresponding measures can be used to reduce the influence of the vibrations. To accurately predict the vibrations induced by train in multi-story buildings, support vector machine (SVM) is used in this paper. Since the parameters in SVM are very vital for the prediction accuracy, shuffled frog-leaping algorithm (SFLA) is used to optimize the parameters for SVM. The proposed model is evaluated with the data from field experiments. The results show SFLA can effectively provide better parameter values for SVM and the SVM models outperform a better performance than artificial neural network (ANN) for train-induced vibration prediction.