To properly simulate hard rock with a high ratio of the uniaxial compressive strength to tensile strength(UCS/TS) and realistic strength-failure envelope,the rock deformation and mechanical characteristics were discus...To properly simulate hard rock with a high ratio of the uniaxial compressive strength to tensile strength(UCS/TS) and realistic strength-failure envelope,the rock deformation and mechanical characteristics were discussed in detail when the particle simulation method with the clump parallel-bond model(CPBM) was used to conduct a series of numerical experiments at the specimen scale.Meanwhile,the effects of the loading procedure and crack density on the mechanical behavior of a specimen,which was modeled by the particle simulation method with the CPBM,were investigated.The related numerical results have demonstrated that:1) The uniaxial compressive strength(UCS),tensile strength(TS) and elastic modulus are overestimated when the conventional loading procedure is used in the particle simulation method with the CPBM; 2) The elastic modulus,strength and UCS/TS decrease,while Poisson ratio increases with the increase of the crack density in the particle simulation method with the CPBM; 3) The particle simulation method with the CPBM can be used to reproduce a high value of UCS/TS(>10),as well as a high friction angle and reasonable cohesion strength; 4) As the confining pressure increases,both the peak strength of the simulated specimen and the number of microscopic cracks increase,but the ratio of tensile cracks number to shear cracks number decreases in the particle simulation method with the CPBM; 5) Compared with the conventional parallel-bond model,the CPBM can be used to reproduce more accurate results for simulating the rock deformation and mechanical characteristics.展开更多
Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accu...Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accuracy of prediction models employing partial least squares(PLS) regression and support vector machine(SVM) regression technique for modeling the penetration rate of TBM. To develop the proposed models, the database that is composed of intact rock properties including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and peak slope index(PSI), and also rock mass properties including distance between planes of weakness(DPW) and the alpha angle(α) are input as dependent variables and the measured ROP is chosen as an independent variable. Two hundred sets of data are collected from Queens Water Tunnel and Karaj-Tehran water transfer tunnel TBM project. The accuracy of the prediction models is measured by the coefficient of determination(R2) and root mean squares error(RMSE) between predicted and observed yield employing 10-fold cross-validation schemes. The R2 and RMSE of prediction are 0.8183 and 0.1807 for SVMR method, and 0.9999 and 0.0011 for PLS method, respectively. Comparison between the values of statistical parameters reveals the superiority of the PLSR model over SVMR one.展开更多
基金Project(11272359) supported by the National Natural Science Foundation of China
文摘To properly simulate hard rock with a high ratio of the uniaxial compressive strength to tensile strength(UCS/TS) and realistic strength-failure envelope,the rock deformation and mechanical characteristics were discussed in detail when the particle simulation method with the clump parallel-bond model(CPBM) was used to conduct a series of numerical experiments at the specimen scale.Meanwhile,the effects of the loading procedure and crack density on the mechanical behavior of a specimen,which was modeled by the particle simulation method with the CPBM,were investigated.The related numerical results have demonstrated that:1) The uniaxial compressive strength(UCS),tensile strength(TS) and elastic modulus are overestimated when the conventional loading procedure is used in the particle simulation method with the CPBM; 2) The elastic modulus,strength and UCS/TS decrease,while Poisson ratio increases with the increase of the crack density in the particle simulation method with the CPBM; 3) The particle simulation method with the CPBM can be used to reproduce a high value of UCS/TS(>10),as well as a high friction angle and reasonable cohesion strength; 4) As the confining pressure increases,both the peak strength of the simulated specimen and the number of microscopic cracks increase,but the ratio of tensile cracks number to shear cracks number decreases in the particle simulation method with the CPBM; 5) Compared with the conventional parallel-bond model,the CPBM can be used to reproduce more accurate results for simulating the rock deformation and mechanical characteristics.
基金Project(2010CB732004)supported by the National Basic Research Program of ChinaProjects(50934006,41272304)supported by the National Natural Science Foundation of China
文摘Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accuracy of prediction models employing partial least squares(PLS) regression and support vector machine(SVM) regression technique for modeling the penetration rate of TBM. To develop the proposed models, the database that is composed of intact rock properties including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and peak slope index(PSI), and also rock mass properties including distance between planes of weakness(DPW) and the alpha angle(α) are input as dependent variables and the measured ROP is chosen as an independent variable. Two hundred sets of data are collected from Queens Water Tunnel and Karaj-Tehran water transfer tunnel TBM project. The accuracy of the prediction models is measured by the coefficient of determination(R2) and root mean squares error(RMSE) between predicted and observed yield employing 10-fold cross-validation schemes. The R2 and RMSE of prediction are 0.8183 and 0.1807 for SVMR method, and 0.9999 and 0.0011 for PLS method, respectively. Comparison between the values of statistical parameters reveals the superiority of the PLSR model over SVMR one.
