“…This indicates that advantageous infiltration occurred during the water infiltration process, which led to a reduction in the time required for the water to reach the deep positions. The viewpoint of dominant infiltration in loess has long been described by many scholars (Xu et al, 2013;Ma et al, 2019a;Zhuang et al, 2020). In the experiments described in this paper, the advantage infiltration mainly occurred in two locations.…”
Section: Large-scale Mudflow and Retrogressive Toe Slidingmentioning
Loess has a strong water sensitivity, so loess landslides often transform into loess mudflows when water is added on the Chinese Loess Plateau, which results in high casualties and property loss of the Chinese government. In this study, a series of flume tests were designed to study the initiation of loess mudflows. The results reveal that the initiation modes of loess mudflows include large-scale mudflow and retrogressive toe sliding (Type A), and small-scale mudflow and retrogressive toe sliding (Type B). A model was used to analyze the test results that describe the effects of water flow on the potential for hillslope failure and liquefaction. It was found that the soil accumulation was unconditionally stable before a loess mudflow was formed, but as the rainfall continued, the water gradually infiltrated the soil, and the soil accumulation changed from unconditionally stable to unconditionally unstable. Thus, this led to different initiation modes during the tests. For Type A, the water preferentially infiltrated into the area with an uneven density and a large amount of water accumulated. The pore water pressure increased quickly and could not dissipate in time, so the loess liquefied. As the liquefaction area continued to expand and became larger, Type A occurred. Relatively speaking, Type B occurs in soil accumulations with relatively uniform densities. These results provide a certain scientific reference for the study of loess mudflows.
“…This indicates that advantageous infiltration occurred during the water infiltration process, which led to a reduction in the time required for the water to reach the deep positions. The viewpoint of dominant infiltration in loess has long been described by many scholars (Xu et al, 2013;Ma et al, 2019a;Zhuang et al, 2020). In the experiments described in this paper, the advantage infiltration mainly occurred in two locations.…”
Section: Large-scale Mudflow and Retrogressive Toe Slidingmentioning
Loess has a strong water sensitivity, so loess landslides often transform into loess mudflows when water is added on the Chinese Loess Plateau, which results in high casualties and property loss of the Chinese government. In this study, a series of flume tests were designed to study the initiation of loess mudflows. The results reveal that the initiation modes of loess mudflows include large-scale mudflow and retrogressive toe sliding (Type A), and small-scale mudflow and retrogressive toe sliding (Type B). A model was used to analyze the test results that describe the effects of water flow on the potential for hillslope failure and liquefaction. It was found that the soil accumulation was unconditionally stable before a loess mudflow was formed, but as the rainfall continued, the water gradually infiltrated the soil, and the soil accumulation changed from unconditionally stable to unconditionally unstable. Thus, this led to different initiation modes during the tests. For Type A, the water preferentially infiltrated into the area with an uneven density and a large amount of water accumulated. The pore water pressure increased quickly and could not dissipate in time, so the loess liquefied. As the liquefaction area continued to expand and became larger, Type A occurred. Relatively speaking, Type B occurs in soil accumulations with relatively uniform densities. These results provide a certain scientific reference for the study of loess mudflows.
“…In Table 2 , the K 0 values of test materials are summarized and it varies between 0.28 to 0.49 with different clay contents. Given a general trend of clay content that increases with loess distribution from north to south in the Chinese Loess Plateau 30 , caution should be exercised in engineering designs at different locations using loess. Figure 9 Comparison of K 0 for loess samples with different clay contents.…”
Compared with conventional soils, such as sand and clay, little knowledge on the coefficient of lateral earth pressure at-rest (K0) has been established for loess in the current literature. This paper presents an experimental investigation on K0 of compacted loess and the associated impacts on undrained shear behaviour. By adopting a K0 consolidation module in the triaxial system, the K0 stress state for loess samples was achieved through a unique feedback control. During the K0 consolidation, the deviatoric stress (q) increases progressively with the premise that the volumetric strain (εv) of the sample equals to the axial strain (εa). The results show that the K0 value of compacted loess is in a range of 0.28 to 0.53, which is dependent on the packing density and the clay content. A distinguishable decrease of K0 was found in the course of K0 consolidation for the loosely compacted loess sample, whereas a similar trend was not observed in the dense sample. In the undrained shear stage, all loess specimens revealed contractive response in the stress path (q-p’) diagram, which can be quantified by a modified collapsibility index (Ic). The index is consistently higher for the K0 consolidated loess samples than for the isotropic ones. The experimental results indicate a strong impact of the initial stress state on the shear behaviour of compacted loess.
“…Loess with well-developed vertical joints, fissures, and large pores, easily fail during times of precipitation (Wang et al, 2019). In addition, fine-grained materials in the loess, such as clay, soluble salts, and carbonates are quickly dissolved when exposed to water (Zhuang et al, 2020). As a result, landslides frequently occur under the effects of rainfall and engineering activity (Figure 2), which seriously threatens the safety of the local people's lives and property.…”
Landslides are widely distributed worldwide and often result in tremendous casualties and economic losses, especially in the Loess Plateau of China. Taking Wuqi County in the hinterland of the Loess Plateau as the research area, using Bayesian hyperparameters to optimize random forest and extreme gradient boosting decision trees model for landslide susceptibility mapping, and the two optimized models are compared. In addition, 14 landslide influencing factors are selected, and 734 landslides are obtained according to field investigation and reports from literals. The landslides were randomly divided into training data (70%) and validation data (30%). The hyperparameters of the random forest and extreme gradient boosting decision tree models were optimized using a Bayesian algorithm, and then the optimal hyperparameters are selected for landslide susceptibility mapping. Both models were evaluated and compared using the receiver operating characteristic curve and confusion matrix. The results show that the AUC validation data of the Bayesian optimized random forest and extreme gradient boosting decision tree model are 0.88 and 0.86, respectively, which showed an improvement of 4 and 3%, indicating that the prediction performance of the two models has been improved. However, the random forest model has a higher predictive ability than the extreme gradient boosting decision tree model. Thus, hyperparameter optimization is of great significance in the improvement of the prediction accuracy of the model. Therefore, the optimized model can generate a high-quality landslide susceptibility map.
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