The shape of rockfalls significantly affects the performance of the impact cushion,which is manifested by the difference in the impact force and the penetration depth of the rockfall during the collision.In this study...The shape of rockfalls significantly affects the performance of the impact cushion,which is manifested by the difference in the impact force and the penetration depth of the rockfall during the collision.In this study,we built the collision numerical model between rockfalls and cushions based on the results from previous studies,and simulated the collision process of rockfalls with four different shapes(cylindrical,cuboid,spherical,and cubic)and different cushions.Essential parameters when rockfalls impact cushions are calculated,including the maximum impact forces on the surface and bottom of the cushions and the maximum penetration depth of the rockfall.The results showed that the maximum impact force on the surface and the bottom of the cushions varies with the rockfall shapes.The maximum impact force on the cushion surface caused by cylindrical rockfall is the smallest,followed by the cuboid rockfall,the cube rockfall,and the spherical rockfall.The maximum impact force at the cushion bottom also follows this trend.However,the penetration depth of cuboid rockfall is the smallest,followed by the cylindrical rockfall,the cubic rockfall,and the spherical rockfall.The results of this study provide more extensive theoretical support for rockfall disaster prevention using gravel cushions.展开更多
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 mechanica...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.展开更多
基金Projects(52174125, 51925402, 52004171) supported by the National Natural Science Foundation of ChinaProject(202103021222008) supported by the Outstanding Youth Cultivation Project in Shanxi Province,ChinaProject(SKLCR20-10) supported by the Open Fund of State Key Laboratory of Coal Resources in Western China。
基金supported by the National Key Research and Development Program of China(2022YFC3080100)the National Natural Science Foundation of China(Grant No.52104125)+2 种基金opening research fund of State Key Laboratory of Geomechanics and Geotechnical Engineering,Institute of Rock and Soil Mechanics,Chinese Academy of Sciences(Grant No.SKLGME021009)the Basic Research Program of Guizhou ProvinceZK[2022]General 166opening fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection(Chengdu University of Technology)(Grant No.SKLGP2022K007)。
文摘The shape of rockfalls significantly affects the performance of the impact cushion,which is manifested by the difference in the impact force and the penetration depth of the rockfall during the collision.In this study,we built the collision numerical model between rockfalls and cushions based on the results from previous studies,and simulated the collision process of rockfalls with four different shapes(cylindrical,cuboid,spherical,and cubic)and different cushions.Essential parameters when rockfalls impact cushions are calculated,including the maximum impact forces on the surface and bottom of the cushions and the maximum penetration depth of the rockfall.The results showed that the maximum impact force on the surface and the bottom of the cushions varies with the rockfall shapes.The maximum impact force on the cushion surface caused by cylindrical rockfall is the smallest,followed by the cuboid rockfall,the cube rockfall,and the spherical rockfall.The maximum impact force at the cushion bottom also follows this trend.However,the penetration depth of cuboid rockfall is the smallest,followed by the cylindrical rockfall,the cubic rockfall,and the spherical rockfall.The results of this study provide more extensive theoretical support for rockfall disaster prevention using gravel cushions.
基金supported by the National Key Research and Development Program of China(2022YFC3080100)the National Natural Science Foundation of China(Nos.52104090,52208328 and 12272353)+1 种基金the Open Fund of Anhui Province Key Laboratory of Building Structure and Underground Engineering,Anhui Jianzhu University(No.KLBSUE-2022-06)the Open Research Fund of Key Laboratory of Construction and Safety of Water Engineering of the Ministry of Water Resources,China Institute of Water Resources and Hydropower Research(Grant No.IWHR-ENGI-202302)。
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.
