Landslide risk assessment(LRA)is of great significance to hazard prevention and mitigation.However,the historical landslide information is incomplete in most areas,which makes the landslide quantitative risk assessmen...Landslide risk assessment(LRA)is of great significance to hazard prevention and mitigation.However,the historical landslide information is incomplete in most areas,which makes the landslide quantitative risk assessment(LQRA)extremely difficult.This research proposed a set of frameworks for LQRA,so as to achieve LQRA in areas with incomplete historical landslide information.Firstly,we constructed the convolutional neural network(CNN)model suitable for landslide susceptibility assessment(LSA)by studying the structure and hyperparameters optimization of CNN.Secondly,we proposed a method to calculate the temporal probability by using the Poisson model based on the time range of historical landslides occurrence,and then conducted landslide hazard assessment(LHA).Then,we established a mathematical model for landslide intensity of shallow landslide based on landslide area and slope,aiming at solving the problem that it is difficult to calculate landslide intensity due to the lack of landslide volume and velocity.Based on the landslide intensity and the hazard-resistant capacity of the element at risk,we assessed the landslide vulnerability.Finally,population risk map and economic risk map are obtained based on the landslide hazard,vulnerability,and estimated value of the elements at risk.The proposed LQRA framework was applied to Tumen City,China for testing and field validation.From the results,the CNN model built can help improve the accuracy of LSA.The proposed temporal probability calculation method is conducive to the completion of LHA in areas with incomplete historical landslide information.The established landslide intensity mathematical model has certain credibility.Since the landslide risk map is obtained through appropriate simplification and substitution estimation,its final value cannot be used as an accurate prediction of future losses,but it can be used as a reference for the extent of potential losses,so as to determine the areas where hazard prevention and mitigation measures need to be taken.展开更多
Split Hopkinson technique has been developed to test the strength of common concrete and steel fiber reinforced concrete under dynamic tensile stress. Two types of test methods are considered, the splitting tensile te...Split Hopkinson technique has been developed to test the strength of common concrete and steel fiber reinforced concrete under dynamic tensile stress. Two types of test methods are considered, the splitting tensile test and a modified spalling test in which a specimen is loaded under uniaxial stress. The result shows that the dynamic strength enhancement of concrete is remarkable by using the reinforcing fiber. But for the common concrete, the base of compressive strength seems to show little effect on the tensile strength under dynamic loading. The experimental results also show that the resistance to tensile fracture of the steel fiber reinforced concrete for C100-mix is higher than those of C40-mix..展开更多
文摘Landslide risk assessment(LRA)is of great significance to hazard prevention and mitigation.However,the historical landslide information is incomplete in most areas,which makes the landslide quantitative risk assessment(LQRA)extremely difficult.This research proposed a set of frameworks for LQRA,so as to achieve LQRA in areas with incomplete historical landslide information.Firstly,we constructed the convolutional neural network(CNN)model suitable for landslide susceptibility assessment(LSA)by studying the structure and hyperparameters optimization of CNN.Secondly,we proposed a method to calculate the temporal probability by using the Poisson model based on the time range of historical landslides occurrence,and then conducted landslide hazard assessment(LHA).Then,we established a mathematical model for landslide intensity of shallow landslide based on landslide area and slope,aiming at solving the problem that it is difficult to calculate landslide intensity due to the lack of landslide volume and velocity.Based on the landslide intensity and the hazard-resistant capacity of the element at risk,we assessed the landslide vulnerability.Finally,population risk map and economic risk map are obtained based on the landslide hazard,vulnerability,and estimated value of the elements at risk.The proposed LQRA framework was applied to Tumen City,China for testing and field validation.From the results,the CNN model built can help improve the accuracy of LSA.The proposed temporal probability calculation method is conducive to the completion of LHA in areas with incomplete historical landslide information.The established landslide intensity mathematical model has certain credibility.Since the landslide risk map is obtained through appropriate simplification and substitution estimation,its final value cannot be used as an accurate prediction of future losses,but it can be used as a reference for the extent of potential losses,so as to determine the areas where hazard prevention and mitigation measures need to be taken.
文摘Split Hopkinson technique has been developed to test the strength of common concrete and steel fiber reinforced concrete under dynamic tensile stress. Two types of test methods are considered, the splitting tensile test and a modified spalling test in which a specimen is loaded under uniaxial stress. The result shows that the dynamic strength enhancement of concrete is remarkable by using the reinforcing fiber. But for the common concrete, the base of compressive strength seems to show little effect on the tensile strength under dynamic loading. The experimental results also show that the resistance to tensile fracture of the steel fiber reinforced concrete for C100-mix is higher than those of C40-mix..
