Prediction of blasting vibration intensity based on Adaboost-SVM combination algorithm

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Journal of Vibration and Shock ›› 2019, Vol. 38 ›› Issue (18) : 231-235.

MEI Bi1，WANG Xuguang2，YANG Renshu1

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Abstract

In order to improve the prediction accuracy of blasting vibration intensity, a prediction method of blasting vibration intensity based on Adaboost-SVM combined algorithm was proposed.By using the principal component analysis method, three main factors were determined from seven kinds of influencing factors of blasting vibration intensity, and the training sample set was established.The SVM prediction model was established by selecting the Gaussian radial basis kernel function.The model parameters were continuously trained and optimized,thus the prediction of blasting vibration intensity was realized.Finally, the prediction model was constructed by Adaboost-SVM combined algorithm, and the prediction accuracy was further improved.It was shown that the prediction accuracy of SVM model is higher than that of traditional empirical formula method and BP neural network method, and the training speed is faster.The proposed Adaboost-SVM combination algorithm can further improve the prediction accuracy to above 97%.

Key words

blasting vibration / prediction / Adaboost / Principal Component Analysis / Support Vector Machine

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MEI Bi1，WANG Xuguang2，YANG Renshu1. Prediction of blasting vibration intensity based on Adaboost-SVM combination algorithm[J]. Journal of Vibration and Shock, 2019, 38(18): 231-235

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