Intelligent Lithology Identification Method of Tunnel Surrounding Rock Based on Machine Learning
In order to realize intelligent,efficient and reliable lithology identification and classification of tunnel surrounding rock,four machine learning algorithms,including k-nearest neighbor,support vector machine,random forest and gradient lifting tree,are used to identify lithology of sandstone,limestone,granite and gneiss.The surrounding rock of the face of the Tongchuan tunnel and the network rock images were used for testing.The average H,S and V values of the rock images were extracted to construct the lithology identification feature space.Based on the principle of machine learning algorithm,the mapping relationship between the feature space and the rock category is established,and the recognition accuracy and running time are taken as evaluation indexes to compare and analyze the recognition effects of the four algorithms.The results show that k-nearest neighbor,random forest and gradient lifting tree all have high recognition accuracy.Considering the algorithm accuracy and efficiency,it is suggested that k-nearest neighbor algorithm should be used as the optimal algorithm.
万方数据
维普
