A series of upward seepage tests on unreinforced and reinforced sand specimens was conducted to investigate the influence of soil density and fibre parameters (i.e. fibre contents and lengths) on the piping failure mode, hydraulic conductivity k, and critical hydraulic gradient i_cr of fibre-reinforced soil (FRS). Direct shear tests were also performed to establish the relationships between soil shear strength and i_cr of FRS. A dataset of seepage tests on FRS was compiled from a literature review to assess the overall variation of k and i_cr with fibre content. The test results revealed that k decreases and i_cr increases as the fibre content increases. The fibre is more effective in dense specimens than in loose specimens. The test results also indicate the i_cr of FRS is strongly correlated to its soil shear strength. Finally, two case examples, unreinforced and reinforced embankments subject to flood-induced elevated water level, were analysed numerically. The numerical results demonstrated that the embankment backfilled with FRS possessed the combined merits of soil improvement in both mechanical and hydraulic performance. The use of FRS as backfill can effectively delay the advance of seepage, reduce soil piping potential, and improve system slope stability against seepage.

Although the mechanisms of slope failure caused by rising groundwater have been widely investigated, the kinematic behavior of landslides in the postfailure stage, which contains essential information for hazard mitigation and risk assessment, has not yet been fully studied. Thus, in this study, a series of numerical simulations using the material point method (MPM) were conducted to analyze the kinematic behavior and soil movement of shallow landslides (infinite slope problems). First, the proposed MPM formulation was validated in a full-scale landslide flume test. The simulated results of final slope profile, runout distance, deposit height, shear band development, slope displacement, and velocity accorded with the experimental results, suggesting that the MPM can quantitatively simulate large deformations. A parametric study of shallow slopes with various hydrological conditions and soil hydraulic and soil mechanical parameters was then performed to assess the influence of the aforementioned factors on landslide kinematics. The simulation results indicated that mechanical behavior at the slope toe is complex; the multiple plastic shear bands generated at the slope toe were due to a combination of shearing and compression. The deposition profile of the slopes was significantly influenced by all input parameters. Among the aforementioned parameters, soil cohesion, location of the groundwater table, and saturated soil permeability most greatly affected runout distance in the sensitivity assessment. Soil friction angle had a minor influence on the kinematic behavior of the slope.

Due to extreme weather, researchers are constantly putting their focus on prevention and mitigation for the impact of disasters in order to reduce the loss of life and property. The disaster associated with slope failures is among the most challenging ones due to the multiple driving factors and complicated mechanisms between them. In this study, a modern space remote sensing technology, InSAR, was introduced as a direct observable for the slope dynamics. The InSAR-derived displacement fields and other in situ geological and topographical factors were integrated, and their correlations with the landslide susceptibility were analyzed. Moreover, multiple machine learning approaches were applied with a goal to construct an optimal model between these complicated factors and landslide susceptibility. Two case studies were performed in the mountainous areas of Taiwan Island and the model performance was evaluated by a confusion matrix. The numerical results revealed that among different machine learning approaches, the Random Forest model outperformed others, with an average accuracy higher than 80%. More importantly, the inclusion of the InSAR data resulted in an improved model accuracy in all training approaches, which is the first to be reported in all of the scientific literature. In other words, the proposed approach provides a novel integrated technique that enables a highly reliable analysis of the landslide susceptibility so that subsequent management or reinforcement can be better planned.