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| Study on the threshold of key joint trace length in rock mass based on mechanical equivalence |
| JIN Aibing1，2，LU Tong1，2，WANG Benxin1，2，SUN Hao1，2，ZHAO Yiqing1，2，CHEN Shuaijun1，2 |
| (1. Key Laboratory of Ministry of Education for Efficient Mining and Safety of Metal Mine，University of Science and Technology Beijing，Beijing 100083，China；2. School of Civil and Resources Engineering，University of Science and Technology Beijing，Beijing 100083，China) |
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Abstract In order to select the key joints from the complex joint network，to provide a scientific quantitative criteria for the selective measurement of structural planes and to simplify the joint network，the threshold value of key joint trace length was studied by taking the trace length as a variable. The random joint network models were prepared by 3D printing technology and based on Monte Carlo theory，and flat specimens of 100 mm ?20 mm ?100 mm were cast. The integrity coefficient，strength，deformation and energy properties of jointed rock mass were analyzed under uniaxial compression, and the threshold value of the key joint trace length was studied based on the mechanical equivalence principle of jointed rock mass. The results show that：(1) The joint trace length is one of the key factors affecting the mechanical properties of specimens. The strength，deformation and energy properties of the specimens do not change significantly when the joints smaller than a certain length are removed. (2) Based on the principal component analysis and Gaussian mixed model clustering algorithm，a mechanical equivalence analysis method of jointed rock masses was established to realize the classification of rock masses with various mechanical parameters，and the differences of the joint network of mechanically unequal samples were analyzed. A set of key joint trace length threshold research method was formed. (3) Considering 9 physical and mechanical parameters，19% of the specimen edge length is considered as the trace length threshold of key joints which significantly impacts the mechanical properties of the specimens. The research results provide a theoretical basis for future artificial intelligence analysis of how to scientifically select joints in jointed rock projects.
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2022, Vol. 41 
