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.

Collapses, landslides, and debris flows are the main geological hazards faced by mankind, which bring heavy losses of life and property to people every year. The purpose of this paper is to establish a method for determining the optimal weighting scheme for multiple geological hazard susceptibility mapping. The information gain ratio (IGR) method was used to analyze the predictive ability of the conditioning factors. The support vector machine (SVM) algorithm was used to evaluate the susceptibility to collapse, landslide, and debris flow of the study area. The receiver operating characteristic curves (ROC) and classification statistics of geological hazard samples were applied to evaluate the performance of the models. The analytic hierarchy process (AHP) and frequency ratio (FR) method were combined to determine the optimal weighting scheme for collapse, landslide, and debris flow. All the conditioning factors have shown a certain predictive ability, making the models of collapse, landslide, and debris flow achieve very good performance. The multiple geological hazard susceptibility maps with the weights of 0.297, 0.539, and 0.164 for collapse, landslide, and debris flow was optimal for this study area with high-precision classification of all the geological hazard samples. The conclusions of this paper could provide meaningful references for risk migration and land use in the study area.

Flexible retaining structure is demonstrated to be an effective measure for debris flow prevention in mountainous areas, which can effectively separate water and stone, reduce particle mass, and dissipate kinetic energy. In order to explore the impulsive force and dynamic response of flexible retaining structure impacted by solid‐liquid two‐phase debris flow, a complex dynamic interaction model of particle‐fluid‐structure has been established by employing the SPH‐DEM‐FEM coupling numerical analysis method. In the present study, the process, impulse force, and dynamic response of a flexible retaining structure subjected to debris flow under different slopes were investigated, respectively. The numerical results are compared with those of the calculation formula of the peak impulsive force of the semiempirical debris flow. Meanwhile, the effectiveness of the coupling numerical simulation is verified. The simulation results show that the coupled SPH‐DEM‐FEM numerical analysis method can visualise the impact of the solid‐liquid two‐phase debris flow on the flexible retaining structure, reproducing the impact, retaining, water‐stone separation, climbing, silting, and deposition process of debris flow. The dynamic time‐history curve of the coupling numerical analysis method for the flexible retaining structure is consistent with the results of the existing literature. The debris flow evaluation results of flexible retaining structure impacted by solid‐liquid two‐phase debris flow are in an order of magnitude with the empirical formula results. Moreover, the results are reliable. The obtained results have a certain reference value for the study on the impulsive force and dynamic response of the flexible retaining structure impacted by solid‐liquid two‐phase debris flow and the engineering design of the flexible retaining structure.

Landslides frequently occur because of natural or human factors. Landslides cause huge losses to the economy as well as human beings every year around the globe. Landslide susceptibility prediction (LSP) plays a key role in the prevention of landslides and has been under investigation for years. Although new machine learning algorithms have achieved excellent performance in terms of prediction accuracy, a sufficient quantity of training samples is essential. In contrast, it is hard to obtain enough landslide samples in most the areas, especially for the county-level area. The present study aims to explore an optimization model in conjunction with conventional unsupervised and supervised learning methods, which performs well with respect to prediction accuracy and comprehensibility. Logistic regression (LR), fuzzy c-means clustering (FCM) and factor analysis (FA) were combined to establish four models: LR model, FCM coupled with LR model, FA coupled with LR model, and FCM, FA coupled with LR model and applied in a specific area. Firstly, an inventory with 114 landslides and 10 conditioning factors was prepared for modeling. Subsequently, four models were applied to LSP. Finally, the performance was evaluated and compared by k-fold cross-validation based on statistical measures. The results showed that the coupled model by FCM, FA and LR achieved the greatest performance among these models with the AUC (Area under the curve) value of 0.827, accuracy of 85.25%, sensitivity of 74.96% and specificity of 86.21%. While the LR model performed the worst with an AUC value of 0.736, accuracy of 77%, sensitivity of 62.52% and specificity of 72.55%. It was concluded that both the dimension reduction and sample size should be considered in modeling, and the performance can be enhanced by combining complementary methods. The combination of models should be more flexible and purposeful. This work provides reference for related research and better guidance to engineering activities, decision-making by local administrations and land use planning.

Reasonable depth of pile embedment is one of the key factors for the success of deep foundation pit projects. This paper has taken a deep foundation pit project in a granite residual soil area in Shenzhen as an example and used physical model tests to study the deformation law of the piles and the surrounding soil during the excavation of the deep foundation pit, revealing the variation law of earth pressure in time and space in the pit and then verified it by numerical simulation. The influence of the embedded depth of the pile on the deformation and earth pressure of the deep foundation pit is then explicitly discussed. The study shows that the embedded depth has a significant effect on the deformation and earth pressure distribution of the foundation pit. The earth pressure in front of the pile tends to approach the passive earth pressure as the embedment depth decreases, while the earth pressure behind the pile is in between the Rankine active earth pressure and the static soil pressure; the settlement value and settlement range of the surrounding soil are doubled. The pile displacement increases as the maximum displacement point rises. The maximum displacement of the pile body was used as the basis for determining the instability of the foundation pit. The optimum embedded depth is obtained when the depth of embedment of the pile is 0.22 H (H is the excavation depth of the foundation pit).