The Longzi River Basin in Tibet is located along the edge of the Himalaya Mountains and is characterized by complex geological conditions and numerous landslides. To evaluate the susceptibility of landslide disasters in this area, eight basic factors were analyzed comprehensively in order to obtain a final susceptibility map. The eight factors are the slope angle, slope aspect, plan curvature, distance-to-fault, distance-to-river, topographic relief, annual precipitation, and lithology. Except for the rainfall factor, which was extracted from the grid cell, all the factors were extracted and classified by the slope unit, which is the basic unit in geological disaster development. The eight factors were superimposed using the information content method (ICM), and the weight of each factor was acquired through an analytic hierarchy process (AHP). The sensitivities of the landslides were divided into four categories: low, moderate, high, and very high, respectively, accounting for 22.76%, 38.64%, 27.51%, and 11.09% of the study area. The accuracies of the area under AUC using slope units and grid cells are 82.6% and 84.2%, respectively, and it means that the two methods are accurate in predicting landslide occurrence. The results show that the high and very high susceptibility areas are distributed throughout the vicinity of the river, with a large component in the north as well as a small portion in the middle and the south. Therefore, it is necessary to conduct landslide warnings in these areas, where the rivers are vast and the population is dense. The susceptibility map can reflect the comprehensive risk of each slope unit, which provides an important reference for later detailed investigations, including research and warning studies.

Soft soil improvement is an important subject in civil engineering, and searching for an effective admixture is an important research. Silica fume (SF) is a kind of recycled material, it can be used in engineering as a pozzolanic material. The main objective of this study is to assess the effectiveness of industrial waste silica fume (SF) as an admixture to improve the cement stabilized soft soil. The unconfined compressive test (UCT) and scanning electron microscopy (SEM) test of cement stabilized soil with different SF contents and different curing times have been carried out. UCT after 28 days revealed that the addition of SF can effectively increase the strength of cement stabilized soil and reduce the amount of cement, and 1.5% SF content is considered optimum, excessive SF will not further increase the strength. SF helped to accelerate the cement hydration reaction and significantly improve the early-age strength of stabilized soil even at 3 days, which can improve construction efficiency in actual projects. SEM analyses shows that the proper SF content could make the hydration product calcium silicate hydrate gel (CSH) fill the pores and increase the strength of the material, but excessive SF will increase the large pores content of the material and reduce the strength. This provided a basis for application of SF in improving soft soil.

Landslides cause huge damage to social economy and human beings every year. Landslide susceptibility mapping (LSM) occupies an important position in land use and risk management. This study is to investigate a hybrid model which makes full use of the advantage of supervised learning model (SLM) and unsupervised learning model (ULM). Firstly, ten continuous variables were used to develop a ULM which consisted of factor analysis (FA) and k-means cluster for a preliminary landslide susceptibility map. Secondly, 351 landslides with “1” label were collected and the same number of non-landslide samples with “0” label were selected from the very low susceptibility area in the preliminary map, constituting a new priori condition for a SLM, and thirteen factors were used for the modeling of gradient boosting decision tree (GBDT) which represented for SLM. Finally, the performance of different models was verified using related indexes. The results showed that the performance of the pretreated GBDT model was improved with sensitivity, specificity, accuracy and the area under the curve (AUC) values of 88.60%, 92.59%, 90.60% and 0.976, respectively. It can be concluded that a pretreated model with strong robustness can be constructed by increasing the purity of samples.