The stability of the surrounding rock mass around cross tunnel in the right bank slope of Dagangshan hydropower station, in the southwestern China, was analyzed by microseismic monitoring as well as numerical simulati...The stability of the surrounding rock mass around cross tunnel in the right bank slope of Dagangshan hydropower station, in the southwestern China, was analyzed by microseismic monitoring as well as numerical simulations. The realistic failure process analysis code （abbreviated as RFPA3D） was employed to reproduce the initiation, propagation, coalescence and interactions of micro-fractures, the evolution of associated stress fields and acoustic emission （AE） activities during the whole failure processes of the surrounding rock mass around cross tunnel. Combined with microseismic activities by microseismic monitoring on the fight bank slope, the spatial-temporal evolution and the micro-fracture precursor characteristics during the complete process of progressive failure of the surrounding rock mass around cross tunnel were discussed and the energy release law of the surrounding rock mass around the cross tunnel was obtained. The result shows that the precursor characteristic of microfractures occurring in rock mass is an effective approach to early warn catastrophic damage of rock mass around cross tunnel. Moreover, the heterogeneity of rock mass is the source and internal cause of the failure precursor of rock mass.展开更多
To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method propose...To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning展开更多
Little research can be found in relation to the stability of anisotropic and heterogenous soils in three dimensions.In this paper,we propose a study on the three-dimensional(3D)undrained slopes in anisotropic and hete...Little research can be found in relation to the stability of anisotropic and heterogenous soils in three dimensions.In this paper,we propose a study on the three-dimensional(3D)undrained slopes in anisotropic and heterogenous clay using advanced upper and lower bounds finite element limit analysis(FELA).The obtained stability solutions are normalized,and presented by a stability number that is a function of three geometrical ratios and two material ratios,i.e.depth ratio,length ratio,slope angle,shear strength gradient ratio and anisotropic strength ratio.Numerical results are compared with experimental data in the literature,and charts are presented to cover a wide range of design parameters.Using the multivariate adaptive regression splines(MARS)analysis,the respective influence and sensitivity of each design parameter on the stability number and the failure mechanism are investigated.An empirical equation is also developed to effectively estimate the stability number.展开更多
基金Projects(50820125405, 51004020, 51174039, 4112265) supported by the National Natural Science Foundation of ChinaProject(201104563) supported by the China Postdoctoral Science Foundation+3 种基金Project(2011CB013503) supported by the National Basic Research Program of ChinaProject(51274053) supported by the Fundamental Research Funds for the Central Universities of ChinaProject(200960) supported by the Foundation for the Author of National Excellent Doctoral Dissertation of ChinaProject(NECT-09-0258) supported by the New Century Excellent Talents in University of China
文摘The stability of the surrounding rock mass around cross tunnel in the right bank slope of Dagangshan hydropower station, in the southwestern China, was analyzed by microseismic monitoring as well as numerical simulations. The realistic failure process analysis code （abbreviated as RFPA3D） was employed to reproduce the initiation, propagation, coalescence and interactions of micro-fractures, the evolution of associated stress fields and acoustic emission （AE） activities during the whole failure processes of the surrounding rock mass around cross tunnel. Combined with microseismic activities by microseismic monitoring on the fight bank slope, the spatial-temporal evolution and the micro-fracture precursor characteristics during the complete process of progressive failure of the surrounding rock mass around cross tunnel were discussed and the energy release law of the surrounding rock mass around the cross tunnel was obtained. The result shows that the precursor characteristic of microfractures occurring in rock mass is an effective approach to early warn catastrophic damage of rock mass around cross tunnel. Moreover, the heterogeneity of rock mass is the source and internal cause of the failure precursor of rock mass.
基金funded by the Natural Science Foundation of China(Grant Nos.41807285,41972280 and 52179103).
文摘To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning
文摘Little research can be found in relation to the stability of anisotropic and heterogenous soils in three dimensions.In this paper,we propose a study on the three-dimensional(3D)undrained slopes in anisotropic and heterogenous clay using advanced upper and lower bounds finite element limit analysis(FELA).The obtained stability solutions are normalized,and presented by a stability number that is a function of three geometrical ratios and two material ratios,i.e.depth ratio,length ratio,slope angle,shear strength gradient ratio and anisotropic strength ratio.Numerical results are compared with experimental data in the literature,and charts are presented to cover a wide range of design parameters.Using the multivariate adaptive regression splines(MARS)analysis,the respective influence and sensitivity of each design parameter on the stability number and the failure mechanism are investigated.An empirical equation is also developed to effectively estimate the stability number.
