文献类型：期刊文献

英文题名：Novel Intelligent Approach for Peak Shear Strength Assessment of Rock Joints on the Basis of the Relevance Vector Machine

作者：Xia, Caichu[1,2];Huang, Man[1,2];Qian, Xin[1];Hong, Chenjie[2];Luo, Zhanyou[3];Du, Shigui[2]

机构：[1]Tongji Univ, Dept Geotech Engn, 1239 Siping Rd, Shanghai 200092, Peoples R China;[2]Shaoxing Univ, Dept Civil Engn, 508 Huancheng West Rd, Shaoxing 312000, Zhejiang, Peoples R China;[3]Zhejiang Univ Sci & Technol, Geotech Engn Inst, 318 Liuhe Rd, Hangzhou 310023, Zhejiang, Peoples R China

年份：2019

卷号：2019

外文期刊名：MATHEMATICAL PROBLEMS IN ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:000522232500002)、、EI(收录号：20200207984004)、Scopus(收录号：2-s2.0-85077399163)、WOS

基金：The study was funded by the Natural Science Foundation of Zhejiang Province (no. LY18D020003) and the National Natural Science Foundation of China (nos. 41327001, 41572299, 41427802). This support was gratefully acknowledged.

语种：英文

外文关键词：Compressive strength - Rocks - Shear strength - Soft computing - Surface roughness - Tensile strength

外文摘要：This study mainly establishes a novel intelligent assessment model for peak shear strength of rock joints based on the relevance vector machine (RVM). RVM is a state-of-the-art soft computing technique that has been rarely utilized in joint shear strength assessment. To establish the hybrid intelligent model, three-dimensional scanning tests and direct shear tests on 36 granite joint specimens were conducted. The peak shear strength ratio (tau p/sigma n) is perceived as an explanation of four types of influencing factors, including joint surface roughness, strength of rock material, basic friction angle, and normal stress. In particular, the compressive strength and tensile strength of rock material are first considered together. A total of 36 experimental data were used in this study to train the RVM model to predict the peak shear strength of rock joints. The performance of the RVM model was assessed using the direct shear test data of rock joints collected from previous researches. Four different kinds of kernel functions were adopted to obtain the optimal model. Results show that the proposed model is significantly efficient in predicting the peak shear strength of rock joints. The proposed model is also a promising tool for peak shear strength of rock joints and provides a new research approach to research the mechanical properties of rock joints.