Rockburst Intensity Prediction based on Kernel Extreme Learning Machine (KELM)

Abstract

As one of the most serious geological disasters in deep underground engineering, rockburst has caused a large number of casualties. However, because of the complex relationship between the inducing factors and rockburst intensity, the problem of rockburst intensity prediction has not been well solved until now. In this study, we collect 292 sets of rockburst data including eight parameters, such as the maximum tangential stress of the surrounding rock σ_θ, the uniaxial compressive strength of the rock σ_c, the uniaxial tensile strength of the rock σ_t, and the strain energy storage index W_et, etc. from more than 20 underground projects as training sets and establish two new rockburst prediction models based on the kernel extreme learning machine (KELM) combined with the genetic algorithm (KELM-GA) and cross-entropy method (KELM-CEM). To further verify the effect of the two models, ten sets of rockburst data from Shuangjiangkou Hydropower Station are selected for analysis and the results show that new models are more accurate compared with five traditional empirical criteria, especially the model based on KELM-CEM which has the accuracy rate of 90%. Meanwhile, the results of 10 consecutive runs of the model based on KELM-CEM are almost the same, meaning that the model has good stability and reliability for engineering applications.