Research on blasting vibration prediction based on BFO-LSSVM and its engineering application

Abstract

In order to realize the accurate prediction of blasting vibration, the LS-SVM optimization model based on BFO algorithm is constructed with the help of bacterial foraging algorithm (BFO) and least squares support vector machine (LS-SVM) theory. 30 groups of blasting data are used as training samples to test the prediction accuracy of the model, and the main factors affecting the propagation of blasting vibration are selected as the input factors, such as single shot charge, blasting center distance, elevation difference, blockage, hole depth and other factors as the input factors, and blasting vibration as the output factor of the prediction model. The results show that the prediction accuracy of BFO-LSSVM model is higher than that of LS-SVM model under the same sample size. Taking the measured vibration data of excavation blasting of Wuqiangxi power station as an example, the average error of BFO-LSSVM model is 5.57%, which verifies the feasibility and practicability of the prediction model.