基于Adaboost-SVM组合算法的爆破振动强度预测研究

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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

Received: 16 October 2018 Published: 15 September 2019

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	Articles by authors
	MEI Bi1
	WANG Xuguang2
	YANG Renshu1

Cite this article:

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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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2019/V38/I18/231

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