巷道围岩稳定性分类的GSM-SVM预测模型

Prediction Model for Stability Classification of Roadway Surrounding Rock Based on Grid Search Method and Support Vector Machine

为准确预测巷道围岩稳定性类别,提出了基于网格搜索法(GSM)优化支持向量机(SVM)的巷道围岩稳定性预测模型。选取22组巷道围岩数据作为学习样本,以水平地应力与巷道夹角、顶板岩性、水的影响和巷道断面积4个指标作为模型输入,巷道围岩稳定程度作为模型输出,同时为增强模型的泛化性能和预测精度,采用改进的网格搜索方法优化支持向量机参数,最终构建基于GSM-SVM的巷道围岩稳定性预测模型。然后运用该模型对8组巷道围岩数据进行预测,并同BP神经网络模型的结果进行对比。结果表明,GSM-SVM模型的预测结果与实际结果吻合,正确率达98%,具有比BP神经网络模型更高的精度。

In order to predict the stability classification of roadway surrounding rock accurately and rapidly, a prediction model tbr stability classification of roadway surrounding rock based on support vector machine （SVM） optimized by grid search method （GSM） was proposed. Twenty-two groups of roadway surrounding rock data were chosen as learning samples, four indexes including angle between the horizontal crustal stress and roadway, roof lithologic character, water effect and basal area of roadway were chosen as model input, and roadway surrounding rock stability was chosen as model output. Meanwhile, in order to improve generalization and prediction precision of the model, the SVM parameters were optimized by improved grid search method, finally the roadway surrounding rock prediction model based on GSM-SVM was established. Then using this model to predict eight groups of roadway surrounding rock data, the prediction results were compared with those predicted by BP neural network model. The results show that the prediction results of GSM-SVM model accord with the actual results, and the correct rate reaches to 98%, so GSM-SVM model has higher accuracy than BP neural network model.