It is important to determine the soil–water characteristic curve (SWCC) for analyzing landslide seepage under varying hydrodynamic conditions. However, the SWCC exhibits high uncertainty due to the variability inherent in soil. To this end, a Bayesian updating framework based on the experimental data was developed to investigate the uncertainty of the SWCC parameters in this study. The objectives of this research were to quantify the uncertainty embedded within the SWCC and determine the critical factors affecting an unsaturated soil landslide under hydrodynamic conditions. For this purpose, a large-scale landslide experiment was conducted, and the monitored water content data were collected. Steady-state seepage analysis was carried out using the finite element method (FEM) to simulate the slope behavior during water level change. In the proposed framework, the parameters of the SWCC model were treated as random variables and parameter uncertainties were evaluated using the Bayesian approach based on the Markov chain Monte Carlo (MCMC) method. Observed data from large-scale landslide experiments were used to calculate the posterior information of SWCC parameters. Then, 95% confidence intervals for the model parameters of the SWCC were derived. The results show that the Bayesian updating method is feasible for the monitoring of data of large-scale landslide model experiments. The establishment of an artificial neural network (ANN) surrogate model in the Bayesian updating process can greatly improve the efficiency of Bayesian model updating.
Measuring the water content of slopes is essential because the distribution and migration of water within slopes are important factors of landslide instability. In this study, the relationship between the resistivity, volumetric temperature water content and temperature of landslide soil was modelled. The model was validated by indoor landslide model tests and field tests in Baijiabao to investigate the effect of reservoir water levels on the water content of landslide slopes. Test results showed that, as the reservoir levels rose, the water content of the landslide soil increased. Moreover, a good correspondence between the measured results and the inversion results based on the resistivity data was obtained by using the high-density electrical method in combination with the developed model of the relationship between resistivity, volumetric water content and temperature, indicating that the proposed method is reliable and practicable in hydrodynamic landslide monitoring.
This paper proposes a new reinforcement structure called soil nets firstly, which is capable of strengthening foundations, slopes, and other structures with better effect than that of soilbags. This proposed geotextile structure typically contains several layers of soil net, which are placed in a unique way. One layer of soil net can be described as a collection of spherical soilbags that are connected together in two directions, one in which they are connected by ropes and another in which they are connected by the PP woven bags that contain the soil. A mechanical property analysis of the soil nets shows that the yield stress of the soil within the soil nets is improved, the tensile capacity of the soil nets is greater than that of the soil with which it is filled, and the equivalent coefficient of interlayer friction between the connected soil nets is larger than that for soilbags. Applications of this new reinforcement structure in the reinforcement of a foundation and a slope are considered, and the corresponding reinforcement effects are calculated. The calculation results demonstrate that the soil nets concept yields efficient reinforcement structures with many advantages.
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