基于光滑粒子流体动力学-有限元法(smoothed particle hydrodynamics-finite element method,SPH-FEM)耦合的数值方法,分别从结构破坏形态、冲击力时程、关键点位移和速度、系统能量等方面,研究含大石块泥石流冲击作用下框架结构房屋的...基于光滑粒子流体动力学-有限元法(smoothed particle hydrodynamics-finite element method,SPH-FEM)耦合的数值方法,分别从结构破坏形态、冲击力时程、关键点位移和速度、系统能量等方面,研究含大石块泥石流冲击作用下框架结构房屋的动力响应和破坏机理。计算结果表明:SPH-FEM耦合方法能够较好地模拟泥石流冲击爬高、绕流扩散、淤积稳定过程。考虑了三种泥石流强度等级,在低、中强度冲击情况下,框架房屋填充墙受到破坏,房屋结构整体保持稳定;在高强度冲击情况下,可以观察到框架房屋的逐步倒塌过程,框架柱损坏模式体现了剪切破坏或塑性铰链失效机制。对于房屋结构而言,泥石流的冲击破坏能力主要来自龙头的冲击力,龙身冲击力相对于龙头降幅约34.2%,大石块的集中作用是结构柱体局部破坏的主要原因。系统能量主要通过泥石流动能转化为结构内能(17.8%)和摩擦耗能(82.8%)。展开更多
The geological and geographical position of the Northwest Himalayas makes it a vulnerable area for mass movements particularly landslides and debris flows. Mass movements have had a substantial impact on the study are...The geological and geographical position of the Northwest Himalayas makes it a vulnerable area for mass movements particularly landslides and debris flows. Mass movements have had a substantial impact on the study area which is extending along Karakorum Highway(KKH) from Besham to Chilas. Intense seismicity, deep gorges, steep terrain and extreme climatic events trigger multiple mountain hazards along the KKH, among which debris flow is recognized as the most destructive geohazard. This study aims to prepare a field-based debris flow inventory map at a regional scale along a 200 km stretch from Besham to Chilas. A total of 117 debris flows were identified in the field, and subsequently, a point-based debris-flow inventory and catchment delineation were performed through Arc GIS analysis. Regional scale debris flow susceptibility and propagation maps were prepared using Weighted Overlay Method(WOM) and Flow-R technique sequentially. Predisposing factors include slope, slope aspect, elevation, Topographic Roughness Index(TRI), Topographic Wetness Index(TWI), stream buffer, distance to faults, lithology rainfall, curvature, and collapsed material layer. The dataset was randomly divided into training data(75%) and validation data(25%). Results were validated through the Receiver Operator Characteristics(ROC) curve. Results show that Area Under the Curve(AUC) using WOM model is 79.2%. Flow-R propagation of debris flow shows that the 13.15%, 22.94%, and 63.91% areas are very high, high, and low susceptible to debris flow respectively. The propagation predicated by Flow-R validates the naturally occurring debris flow propagation as observed in the field surveys. The output of this research will provide valuable input to the decision makers for the site selection, designing of the prevention system, and for the protection of current infrastructure.展开更多
The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques we...The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.展开更多
文摘基于光滑粒子流体动力学-有限元法(smoothed particle hydrodynamics-finite element method,SPH-FEM)耦合的数值方法,分别从结构破坏形态、冲击力时程、关键点位移和速度、系统能量等方面,研究含大石块泥石流冲击作用下框架结构房屋的动力响应和破坏机理。计算结果表明:SPH-FEM耦合方法能够较好地模拟泥石流冲击爬高、绕流扩散、淤积稳定过程。考虑了三种泥石流强度等级,在低、中强度冲击情况下,框架房屋填充墙受到破坏,房屋结构整体保持稳定;在高强度冲击情况下,可以观察到框架房屋的逐步倒塌过程,框架柱损坏模式体现了剪切破坏或塑性铰链失效机制。对于房屋结构而言,泥石流的冲击破坏能力主要来自龙头的冲击力,龙身冲击力相对于龙头降幅约34.2%,大石块的集中作用是结构柱体局部破坏的主要原因。系统能量主要通过泥石流动能转化为结构内能(17.8%)和摩擦耗能(82.8%)。
基金financially supported by the Higher Education Commission of Pakistan (HEC) grant under National Research Program for Universities (NRPU) with No: (20-14681/NRPU/R&D/HEC/20212021)。
文摘The geological and geographical position of the Northwest Himalayas makes it a vulnerable area for mass movements particularly landslides and debris flows. Mass movements have had a substantial impact on the study area which is extending along Karakorum Highway(KKH) from Besham to Chilas. Intense seismicity, deep gorges, steep terrain and extreme climatic events trigger multiple mountain hazards along the KKH, among which debris flow is recognized as the most destructive geohazard. This study aims to prepare a field-based debris flow inventory map at a regional scale along a 200 km stretch from Besham to Chilas. A total of 117 debris flows were identified in the field, and subsequently, a point-based debris-flow inventory and catchment delineation were performed through Arc GIS analysis. Regional scale debris flow susceptibility and propagation maps were prepared using Weighted Overlay Method(WOM) and Flow-R technique sequentially. Predisposing factors include slope, slope aspect, elevation, Topographic Roughness Index(TRI), Topographic Wetness Index(TWI), stream buffer, distance to faults, lithology rainfall, curvature, and collapsed material layer. The dataset was randomly divided into training data(75%) and validation data(25%). Results were validated through the Receiver Operator Characteristics(ROC) curve. Results show that Area Under the Curve(AUC) using WOM model is 79.2%. Flow-R propagation of debris flow shows that the 13.15%, 22.94%, and 63.91% areas are very high, high, and low susceptible to debris flow respectively. The propagation predicated by Flow-R validates the naturally occurring debris flow propagation as observed in the field surveys. The output of this research will provide valuable input to the decision makers for the site selection, designing of the prevention system, and for the protection of current infrastructure.
基金supported by the Second Tibetan Plateau Scientific Expedition and Research Program(Grant no.2019QZKK0904)Natural Science Foundation of Hebei Province(Grant no.D2022403032)S&T Program of Hebei(Grant no.E2021403001).
文摘The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.
