Abstract:
To solve the problems of outlier samples, unbalanced samples, and local optimum of sparrow search algorithm in machine learning rockburst prediction, this paper established a rockburst prediction model from two perspectives of data preprocessing and algorithm improvement. First, based on lithology conditions and stress conditions, selected the maximum tangential stress, compression strength, tensile strength and elastic energy index of surrounding rock as the characteristic indexes, and used three kinds of machine learning algorithms combined with 5-fold cross-validation method to construct the prediction model. In the data pre-processing stage, collected 174 groups of domestic and international rock burst cases to establish a database; for outlier samples, introduced the local anomaly factor (LOF) algorithm to detect and eliminate outlier samples step by step according to the rock burst class; for sample imbalance, introduced the adaptive oversampling method (ADASYN) to increase the number of minority class samples.Three hybrid strategies were used to improve Sparrow search algorithm, and ISSA was used to optimize parameters of three machine learning algorithms, namely limit Gradient Lift Tree (XGBoost), Random forest (RF) and multi-layer perceptron (MLP). Multiple evaluation indexes such as accuracy rate and precision rate were analyzed and discussed to verify the effectiveness of the model. The results show that the accuracy of the newly constructed optimal model ISA-XGBoost reaches 94.12%, which has a high prediction accuracy. In addition to the feature importance analysis of the four feature indexes, it is determined that the maximum tangential stress of the surrounding rock is the most important feature.
点击查看大图
下载:
