The stability of reservoir bank slopes during the impoundment period has become a critical issue in the construction and operation of large-scale hydropower projects.A predictive and early warning method for reservoir...The stability of reservoir bank slopes during the impoundment period has become a critical issue in the construction and operation of large-scale hydropower projects.A predictive and early warning method for reservoir bank slopes is proposed,based on slip resistance stability evolution analysis.Using a refined three-dimensional numerical calculation model of the bank slope,the creep damage model is employed for simulation and analysis,enabling the derivation of stress field and strain field evolution from bank slope excavation to the long-term impoundment period.Subsequently,for the stress field of the bank slope at any given moment,the safety factors of the sliding blocks are determined by using the multigrid method and vector sum method.Accordingly,the evolutionary law of the sliding safety factor for the bank slope can be derived.By integrating the long-term stability evolution trend of the slope with specific engineering practices,the safety factors for graded warning can be determined.Based on the time correspondence,the graded warning moment and the deformation warning index for slope measurement points can be determined.In this study,the proposed method is applied to the left bank slope of the Jinping I Hydropower Station.The results indicate that from excavation to June 2022,the left bank slope exhibits a strong correlation with excavation elevation and the number of reservoir water cycles.The initial,maximum,and minimum safety factors are 2.01,3.07,and 1.58,respectively.The deep fracture SL44-1 serves as the primary stress-bearing slip surface of the left bank slope,while the safety margin of the fault f42-9 and lamprophyre X is slightly insufficient.Based on the long-term stability evolution trend of the slope and in accordance with relevant standards,the safety factors for graded warning indicators—K_(w1),K_(w2),K_(w3),and K_(w4)—are determined as 1.350,1.325,1.300,and 1.275,respectively.Correspondingly,the estimated warning times are 12/30/2066,12/30/2084,and 12/30/2120.Accordingly,the deformation graded warning indexes for slope measurement points are established.展开更多
Landslides are one of the geological disasters with wide distribution,high impact and serious damage around the world.Landslide risk assessment can help us know the risk of landslides occurring,which is an effective w...Landslides are one of the geological disasters with wide distribution,high impact and serious damage around the world.Landslide risk assessment can help us know the risk of landslides occurring,which is an effective way to prevent landslide disasters in advance.In recent decades,artificial intelligence(AI)has developed rapidly and has been used in a wide range of applications,especially for natural hazards.Based on the published literatures,this paper presents a detailed review of AI applications in landslide risk assessment.Three key areas where the application of AI is prominent are identified,including landslide detection,landslide susceptibility assessment,and prediction of landslide displacement.Machine learning(ML)containing deep learning(DL)has emerged as the primary technology which has been considered successfully due to its ability to quantify complex nonlinear relationships of soil structures and landslide predisposing factors.Among the algorithms,convolutional neural networks(CNNs)and recurrent neural networks(RNNs)are two models that are most widely used with satisfactory results in landslide risk assessment.The generalization ability,sampling training strategies,and hyperparameters optimization of these models are crucial and should be carefully considered.The challenges and opportunities of AI applications are also fully discussed to provide suggestions for future research in landslide risk assessment.展开更多
The Qinghai-Tibet Plateau is a climate-sensitive region.The characteristics of drought and flood events in this region are significantly different as compared to other areas in the country,which could potentially indu...The Qinghai-Tibet Plateau is a climate-sensitive region.The characteristics of drought and flood events in this region are significantly different as compared to other areas in the country,which could potentially induce a series of water security,ecological and environmental problems.It is urgent that innovative theories and methods for estimation of drought and flood disasters as well as their adaptive regulations are required.Based on extensive literature review,this paper identifies new situations of the evolution of drought and flood events on the Qinghai-Tibet Plateau,and analyzes the research progress in terms of monitoring and simulation,forecasting and early warning,risk prevention and emergency response.The study found that there were problems such as insufficient integration of multi-source data,low accuracy of forecasting and early warning,unclear driving mechanisms of drought and flood disaster chains,and lack of targeted risk prevention and regulation measures.On this basis,future research priorities are proposed,and the possible research and development paths are elaborated,including the evolution law of drought and flood on the Qinghai-Tibet Plateau,the coincidence characteristics of drought and flood from the perspective of a water resources system,prediction and early warning of drought and flood coupled with numerical simulation and knowledge mining,identification of risk blocking points of drought and flood disaster chain and the adaptive regulations.Hopefully,the paper will provide technical support for preventing flood and drought disasters,water resources protection,ecological restoration and climate change adaptation on the Qinghai-Tibet Plateau.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.41961134032).
文摘The stability of reservoir bank slopes during the impoundment period has become a critical issue in the construction and operation of large-scale hydropower projects.A predictive and early warning method for reservoir bank slopes is proposed,based on slip resistance stability evolution analysis.Using a refined three-dimensional numerical calculation model of the bank slope,the creep damage model is employed for simulation and analysis,enabling the derivation of stress field and strain field evolution from bank slope excavation to the long-term impoundment period.Subsequently,for the stress field of the bank slope at any given moment,the safety factors of the sliding blocks are determined by using the multigrid method and vector sum method.Accordingly,the evolutionary law of the sliding safety factor for the bank slope can be derived.By integrating the long-term stability evolution trend of the slope with specific engineering practices,the safety factors for graded warning can be determined.Based on the time correspondence,the graded warning moment and the deformation warning index for slope measurement points can be determined.In this study,the proposed method is applied to the left bank slope of the Jinping I Hydropower Station.The results indicate that from excavation to June 2022,the left bank slope exhibits a strong correlation with excavation elevation and the number of reservoir water cycles.The initial,maximum,and minimum safety factors are 2.01,3.07,and 1.58,respectively.The deep fracture SL44-1 serves as the primary stress-bearing slip surface of the left bank slope,while the safety margin of the fault f42-9 and lamprophyre X is slightly insufficient.Based on the long-term stability evolution trend of the slope and in accordance with relevant standards,the safety factors for graded warning indicators—K_(w1),K_(w2),K_(w3),and K_(w4)—are determined as 1.350,1.325,1.300,and 1.275,respectively.Correspondingly,the estimated warning times are 12/30/2066,12/30/2084,and 12/30/2120.Accordingly,the deformation graded warning indexes for slope measurement points are established.
基金supported by the National Natural Science Foundation of China(U2240221 and 52379105)the Sichuan Youth Science and Technology Innovation Research Team Project(2020JDTD0006)。
文摘Landslides are one of the geological disasters with wide distribution,high impact and serious damage around the world.Landslide risk assessment can help us know the risk of landslides occurring,which is an effective way to prevent landslide disasters in advance.In recent decades,artificial intelligence(AI)has developed rapidly and has been used in a wide range of applications,especially for natural hazards.Based on the published literatures,this paper presents a detailed review of AI applications in landslide risk assessment.Three key areas where the application of AI is prominent are identified,including landslide detection,landslide susceptibility assessment,and prediction of landslide displacement.Machine learning(ML)containing deep learning(DL)has emerged as the primary technology which has been considered successfully due to its ability to quantify complex nonlinear relationships of soil structures and landslide predisposing factors.Among the algorithms,convolutional neural networks(CNNs)and recurrent neural networks(RNNs)are two models that are most widely used with satisfactory results in landslide risk assessment.The generalization ability,sampling training strategies,and hyperparameters optimization of these models are crucial and should be carefully considered.The challenges and opportunities of AI applications are also fully discussed to provide suggestions for future research in landslide risk assessment.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFC3201705)。
文摘The Qinghai-Tibet Plateau is a climate-sensitive region.The characteristics of drought and flood events in this region are significantly different as compared to other areas in the country,which could potentially induce a series of water security,ecological and environmental problems.It is urgent that innovative theories and methods for estimation of drought and flood disasters as well as their adaptive regulations are required.Based on extensive literature review,this paper identifies new situations of the evolution of drought and flood events on the Qinghai-Tibet Plateau,and analyzes the research progress in terms of monitoring and simulation,forecasting and early warning,risk prevention and emergency response.The study found that there were problems such as insufficient integration of multi-source data,low accuracy of forecasting and early warning,unclear driving mechanisms of drought and flood disaster chains,and lack of targeted risk prevention and regulation measures.On this basis,future research priorities are proposed,and the possible research and development paths are elaborated,including the evolution law of drought and flood on the Qinghai-Tibet Plateau,the coincidence characteristics of drought and flood from the perspective of a water resources system,prediction and early warning of drought and flood coupled with numerical simulation and knowledge mining,identification of risk blocking points of drought and flood disaster chain and the adaptive regulations.Hopefully,the paper will provide technical support for preventing flood and drought disasters,water resources protection,ecological restoration and climate change adaptation on the Qinghai-Tibet Plateau.
