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| 1 | Effects of confining pressure on acoustic emission and failure characteristics of sandstone显示文摘In this study,uniaxial and triaxial compression acoustic emission(AE)tests were implemented to investigate the AE effect and failure characteristics of sandstone under different confining pressures(σ3).The evolution of AE parameters in the rock failure process and fracture fractal dimension characteristics after failure were analyzed.The results revealed that the activity of the AE signal is strongly related toσ3.The evolution of the Ib value can be divided into the I-fluctuation,II-stability,and III-decrease stages.In the first stage,the Ib value of the AE was relatively high,and the AE energy was low.Then,the Ib value tended to be stable;however,the fluctuation amplitude decreased,and the AE energy rapidly increased.In the stage of decrease,the AE energy sharply increased before the load approached the peak value,and the Ib value significantly decreased and dropped to the lowest point before the peak value.Asσ3 increased,the rock’s failure mode changed from tensile failure to shear failure and became more coordinated.As the confining pressure increased,the shape dimension decreased,and the order degree of rock failure increased.The confining pressure exerted a certain control effect on the rock failure. | Zhen Huang Qixiong Gu Yufan Wu Yun Wu Shijie Li Kui Zhao Rui Zhang | 2021 | International Journal of Mining Science and Technology2021，31，5： | 6 |
| 2 | 统计参数表征岩体结构面粗糙度研究进展显示文摘岩体结构面粗糙度定量化表征是精准评估其力学性质的关键。从目前的研究成果看,统计参数是表征结构面粗糙度的主要方法之一,且成果丰富。综述该领域成果,详细介绍了结构面粗糙度各统计参数表征方法;指出了采样间隔是统计参数表征结构面粗糙度的重要影响因素;围绕这一难题,分析总结了目前解决这一问题的方案;最后介绍了考虑采样间隔影响的统计参数表征结构面粗糙度最新研究进展,并指出统计参数联合采样间隔共同表征结构面粗糙度计算模型的研究可能是解决这一问题的有效途径之一。 | 陈世江 姜政 杨军伟 魏中举 李健 杨付领 封红欢 | 2023 | 六盘水师范学院学报2023，35，5： | 0 |
| 3 | A method to predict the peak shear strength of rock joints based on machine learning显示文摘In geotechnical and tunneling engineering,accurately determining the mechanical properties of jointed rock holds great significance for project safety assessments.Peak shear strength(PSS),being the paramount mechanical property of joints,has been a focal point in the research field.There are limitations in the current peak shear strength(PSS)prediction models for jointed rock:(i)the models do not comprehensively consider various influencing factors,and a PSS prediction model covering seven factors has not been established,including the sampling interval of the joints,the surface roughness of the joints,the normal stress,the basic friction angle,the uniaxial tensile strength,the uniaxial compressive strength,and the joint size for coupled joints;(ii)the datasets used to train the models are relatively limited;and(iii)there is a controversy regarding whether compressive or tensile strength should be used as the strength term among the influencing factors.To overcome these limitations,we developed four machine learning models covering these seven influencing factors,three relying on Support Vector Regression(SVR)with different kernel functions(linear,polynomial,and Radial Basis Function(RBF))and one using deep learning(DL).Based on these seven influencing factors,we compiled a dataset comprising the outcomes of 493 published direct shear tests for the training and validation of these four models.We compared the prediction performance of these four machine learning models with Tang’s and Tatone’s models.The prediction errors of Tang’s and Tatone’s models are 21.8%and 17.7%,respectively,while SVR_linear is at 16.6%,SVR_poly is at 14.0%,and SVR_RBF is at 12.1%.DL outperforms the two existing models with only an 8.5%error.Additionally,we performed shear tests on granite joints to validate the predictive capability of the DL-based model.With the DL approach,the results suggest that uniaxial tensile strength is recommended as the material strength term in the PSS model for more reliable outcomes. | BAN Li-ren ZHU Chun HOU Yu-hang DU Wei-sheng QI Cheng-zhi LU Chun-sheng | 2023 | Journal of Mountain Science2023，20，12： | 0 |
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