地质与勘探2024，Vol.60Issue(2)：388-406,19.DOI:10.12134/j.dzykt.2024.02.016
基于机器学习的岩体结构面剪切破坏区域预测研究
Prediction of Shear Failure Zones of Rock Structural Planes Based on Machine Learning
摘要
Abstract
The shear failure zone,as the main contact area for the relative motion of the upper and lower plates of the structural plane,has a significant impact on the shearing strength.According to the highly nonlinear relationship between the shear failure area and the morphology characteristics of structural planes,this paper analyzes the surface morphology characteristics and shear mechanism of structural planes,and describes the surface morphology characteristics of structural planes using roughness parameters such as inclination,dip angle,curvature,elevation difference,and aperture distribution.Direct shear tests were conducted on structural plane samples under a normal stress of 1.0 MPa,and the shear failure zone was extracted using image segmentation techniques.Various machine learning methods were used to build predictive models for the shear failure zone of the structural plane,establishing a nonlinear relationship between the roughness parameters of the structural plane and its failure state.Predictive performance of the models was assessed by using indicators like training accuracy and the AUC(Area Under Curve)value.The results show that the integrated bagging trees in the established model had the best predictive performance,followed by K-nearest neighbors,with the highest training accuracy reaching 98.02%and 97.38%,respectively,and the highest AUC values being 0.78 and 0.74,respectively.Sensitivity analysis reveals that aperture distribution had the most significant impact on the shear failure zone.This research is of great significance for effectively analyzing the shear failure mechanism of rock structural plane and accurately evaluating their shear strength.关键词
岩体结构面/粗糙度/剪切破坏区域/机器学习/直剪试验Key words
rock structural plane/roughness/shear failure zone/machine learning/direct shear test分类
建筑与水利引用本文复制引用
王松挺,王昌硕,杜时贵,罗战友,雍睿..基于机器学习的岩体结构面剪切破坏区域预测研究[J].地质与勘探,2024,60(2):388-406,19.基金项目
国家自然科学基金"卸荷作用下无充填结构面形貌演变规律及剪切强度劣化模型研究"(编号:42207175)和宁波市自然科学基金"无充填结构面三维频谱粗糙结构剪切劣化机理研究"(编号:2022J115)联合资助. （编号:42207175）
