Copper alloyed with various compositions of nickel and tin were cast into molds under argon atmosphere.The cast rods were homogenized,solution heat treated,followed by aging for different time duration.The specimens w...Copper alloyed with various compositions of nickel and tin were cast into molds under argon atmosphere.The cast rods were homogenized,solution heat treated,followed by aging for different time duration.The specimens were characterized for microstructure and tested for microhardness and wear rate.A hybrid model with a linear function and radial basis function was developed to analyze the influence of nickel,tin,and aging time on the microhardness and tribological behavior of copper-nickel-sin alloy system.The results indicate that increase in the composition of nickel and tin increases the microhardness and decreases the wear rate of the alloy.The increase in the concentration of nickel and tin decreases the peak aging time of the alloy system.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
文摘Copper alloyed with various compositions of nickel and tin were cast into molds under argon atmosphere.The cast rods were homogenized,solution heat treated,followed by aging for different time duration.The specimens were characterized for microstructure and tested for microhardness and wear rate.A hybrid model with a linear function and radial basis function was developed to analyze the influence of nickel,tin,and aging time on the microhardness and tribological behavior of copper-nickel-sin alloy system.The results indicate that increase in the composition of nickel and tin increases the microhardness and decreases the wear rate of the alloy.The increase in the concentration of nickel and tin decreases the peak aging time of the alloy system.
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.
