Rock burst prediction based on genetic algorithms and extreme learning machine

Abstract

Rock burst is a kind of geological disaster in rock excavation of high stress areas. To evaluate intensity of rock burst, the maximum shear stress, uniaxial compressive strength, uniaxial tensile strength and rock elastic energy index were selected as input factors, and burst pit depth as output factor. The rock burst prediction model was proposed according to the genetic algorithms and extreme learning machine. The effect of structural surface was taken into consideration. Based on the engineering examples of tunnels, the observed and collected data were divided into the training set, validation set and prediction set. The training set and validation set were used to train and optimize the model. Parameter optimization results are presented. The hidden layer node was 450, and the fitness of the predictions was 0.0197 under the optimal combination of the input weight and offset vector. Then, the optimized model is tested with the prediction set. Results show that the proposed model is effective. The maximum relative error is 4.71%, and the average relative error is 3.20%, which proves that the model has practical value in the relative engineering.