Foamed concrete as energy absorption material for high geo-stress soft rock tunnels has been proven to be feasible due to its high compressibility and lightweight.However,the lengthy curing and defoaming problems caus...Foamed concrete as energy absorption material for high geo-stress soft rock tunnels has been proven to be feasible due to its high compressibility and lightweight.However,the lengthy curing and defoaming problems caused by the cast-in-place method of large-volume foamed concrete remain unsolved.In this study,we propose a novel energy absorber composed of foamed concrete-filled polyethylene(FC-PE)pipe and analyze its deformation and energy absorption capacity via quasi-static lateral compression experiments.Results show that FC-PE pipes exhibit typical three-stage deformation characteristics,comprising the elastic stage,the plastic plateau,and the densification stage.Furthermore,the plateau stress,energy absorption,and specific energy absorption of the specimens are 0.81–1.91 MPa,164–533 J,and 1.4–3.6 J/g,respectively.As the density of the foamed concrete increases,the plateau stress and energy absorption increase significantly.Conversely,the length of the plastic plateau and energy absorption efficiency decrease.Moreover,based on the vertical slice method,progressive compression of core material,and the 6 plastic hinges deformation mechanism of the pipe wall,a theoretical calculation method for effective energy absorption is established and achieves good agreement with experimental results,which is beneficial to the optimization of the composite structure.展开更多
Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50...Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50） resulting from rock blast fragmentation in various mines based on the statistical learning theory. The data base consisted of blast design parameters, explosive parameters, modulus of elasticity and in-situ block size. The seven input independent variables used for the SVMs model for the prediction of X50 of rock blast fragmentation were the ratio of bench height to drilled burden （H/B）, ratio of spacing to burden （S/B）, ratio of burden to hole diameter （B/D）, ratio of stemming to burden （T/B）, powder factor （Pf）, modulus of elasticity （E） and in-situ block size （XB）. After using the 90 sets of the measured data in various mines and rock formations in the world for training and testing, the model was applied to 12 another blast data for validation of the trained support vector regression （SVR） model. The prediction results of SVR were compared with those of artificial neural network （ANN）, multivariate regression analysis （MVRA） models, conventional Kuznetsov method and the measured X50 values. The proposed method shows promising results and the prediction accuracy of SVMs model is acceptable.展开更多
基金The authors gratefully acknowledge the support of National Natural Science Foundation of China(No.51991392)the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)of China(No.2019QZKK0904)+1 种基金the National Outstanding Youth Science Fund Project of National Natural Science Foundation of China(No.51922104)Youth Innovation Promotion Association CAS.
文摘Foamed concrete as energy absorption material for high geo-stress soft rock tunnels has been proven to be feasible due to its high compressibility and lightweight.However,the lengthy curing and defoaming problems caused by the cast-in-place method of large-volume foamed concrete remain unsolved.In this study,we propose a novel energy absorber composed of foamed concrete-filled polyethylene(FC-PE)pipe and analyze its deformation and energy absorption capacity via quasi-static lateral compression experiments.Results show that FC-PE pipes exhibit typical three-stage deformation characteristics,comprising the elastic stage,the plastic plateau,and the densification stage.Furthermore,the plateau stress,energy absorption,and specific energy absorption of the specimens are 0.81–1.91 MPa,164–533 J,and 1.4–3.6 J/g,respectively.As the density of the foamed concrete increases,the plateau stress and energy absorption increase significantly.Conversely,the length of the plastic plateau and energy absorption efficiency decrease.Moreover,based on the vertical slice method,progressive compression of core material,and the 6 plastic hinges deformation mechanism of the pipe wall,a theoretical calculation method for effective energy absorption is established and achieves good agreement with experimental results,which is beneficial to the optimization of the composite structure.
基金Foundation item:Project (2006BAB02A02) supported by the National Key Technology R&D Program during the 11th Five-year Plan Period of ChinaProject (CX2011B119) supported by the Graduated Students' Research and Innovation Fund of Hunan Province, ChinaProject (2009ssxt230) supported by the Central South University Innovation Fund,China
文摘Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50） resulting from rock blast fragmentation in various mines based on the statistical learning theory. The data base consisted of blast design parameters, explosive parameters, modulus of elasticity and in-situ block size. The seven input independent variables used for the SVMs model for the prediction of X50 of rock blast fragmentation were the ratio of bench height to drilled burden （H/B）, ratio of spacing to burden （S/B）, ratio of burden to hole diameter （B/D）, ratio of stemming to burden （T/B）, powder factor （Pf）, modulus of elasticity （E） and in-situ block size （XB）. After using the 90 sets of the measured data in various mines and rock formations in the world for training and testing, the model was applied to 12 another blast data for validation of the trained support vector regression （SVR） model. The prediction results of SVR were compared with those of artificial neural network （ANN）, multivariate regression analysis （MVRA） models, conventional Kuznetsov method and the measured X50 values. The proposed method shows promising results and the prediction accuracy of SVMs model is acceptable.
