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AbstractLandslides are one natural disaster in the mountains, causing damage to properties, destruction of infrastructure and loss of life. While landslide deformation and displacement are commonly observed through experimental investigation of the prototype models, this time-consuming approach might involve human errors and inaccurate constitutive models. This study aims to propose a numerical approach based on machine learning for substituting experimental investigation. In pursuit of this objective, the probability-weighted moments (PWMs) for generalised extreme value distribution (GEVD) were implemented to predict the deposit landslide deformation under uniformly increasing rainfall. The deposit landslide of the Dahua was selected as the case study. The accuracy and reliability of the proposed PWM are validated through the maximum likelihood method and the Jenkinson method. The PWM with GEVD is used to derive the progressive failure of the deposit landslide stability under the return period, the pore water pressure variation and the return period’s effect. The results show that the proposed solution is suitable for predicting the responses of landslides. Moreover, the results demonstrate the importance of soil density; 30% of the deposited soil was moved when the soil density was 2 g/cm3besides, and more than 90% were moved at the top of the slope for the soil density equal to 3 g/cm3.

AbstractThe hydro-mechanical behaviour and the constitutive model of sandy mudstone were investigated in this article. Two groups of hydro-mechanical triaxial compression tests were conducted. The results indicated that the inelastic deformation, damage, permeability and failure of the sandy mudstone were affected by the pore pressure and confining pressure. The hydro-mechanical behaviour was characterised by the coupled elastoplastic damage model. This model was based on the effective stress principle and numerical return mapping algorithms. Sensitivity analysis was also performed to examine the effects of four key parameters on the modelling of elastoplastic and damage behaviour. This study can provide a foundation for physical understanding and mathematical modelling of sandy mudstone under coupled loads.

AbstractSpatial variability of properties of geo-materials has a significant impact on the stability of landslides. In this paper, the spatial variability of soil properties of a landslide is modelled using random-field generated by the discrete cosine transform. The method developed can be readily applied to determine the probability of failure of the landslide using Monte Carlo simulation. A case study of a landslide with spatially variable properties is performed and discussed. The proposed approach is shown to be effective and efficient for probabilistic stability analysis of landslides modelled using 2-D random-field.

AbstractUncertainties of geotechnical parameters in landslide engineering mainly arise from their spatial variabilities, measuring error and inaccurate constitutive model. Interval limit equilibrium method (ILEM) is used to analyse the stability of landslides with uncertain geotechnical parameters. However, the method suffers from the issue of overestimation in the calculation of the safety factor (FS) interval while using the basic interval operations. To overcome the limitation, an improved ILEM based on particle swarm optimisation (ILEM-PSO) is proposed in the article. In the method, the uncertain parameters and their intervals are taken as the search variables and search space of PSO, and the FS functions are used as the fitness functions. A predefined number of iterations are executed to search the minimal and maximal FS, which are the lower and upper bound of the FS interval. The proposed method is validated with a testing function and ACADS benchmark question EX11, and then applied to the stability analysis of Dahua landslide. Comparisons with ILEM and Monte Carlo simulation are also given in the article. Results show that the proposed method is effective in solving the issue of overestimation in ILEM and obtains accurate FS intervals of landslides.

AbstractDisplacement prediction is a direct and effective method for mitigating geohazards. Due to the influence of rainfall and reservoir water level variations, landslides often display step-like deformations with an increasing trend and periodic fluctuation, indicating long-term memory in displacement time series. Traditional data-driven methods are mostly suitable for short-term prediction, and extra data processing is applied to solve this problem. This paper proposes a novel deep learning-based displacement prediction method using long short-term memory (LSTM) networks. Based on open-source frameworks for deep learning, namely, Keras and TensorFlow, a detailed implementation of displacement prediction is proposed and illustrated. The Baishuihe landslide, a typical landslide with long-term monitoring, is taken as a case study, and both single-factor and multi-factor predictions are performed. The results indicate that multi-factor prediction can reduce overfitting and improve accuracy. Compared with the existing method, the multi-factor deep-learning model displays better performance. This study indicates that the LSTM-based deep-learning model is suitable and convenient for displacement prediction and has broad prospects in safety monitoring of water-induced landslides.

AbstractPre-existing joints within a rock mass has a significant effect on the hydro-mechanical properties of the rock mass. The study investigates the hydro-mechanical properties of rock-like materials with intermittent joints under different confining and hydraulic pressures. Cylindrical specimens containing two sets of parallel pre-existing joints were prepared, and the hydro-mechanical experiments under different hydraulic pressures were carried out using a servo-controlled rock triaxial testing system. The test results show that the strength, permeability and deformation properties of the specimens were influenced by the hydraulic pressure. The four stages of evolution of permeability in the deformation process is closely correlated with the development of joints and cracks within the specimen. The relations between volumetric strain and permeability were analyzed and discussed. It is found that the exponential function describes the permeability and volumetric strain relation well in the contraction stage. When the fluid pressure is high, the fluid pressure has a larger impact on the specimen failure mode than the pre-existing thin joints within the specimen in this test.

AbstractA triaxial unloading mechanical test is conducted on sandstone considering different initial confining pressures and unloading rates under the effect of hydraulic coupling. The energy evolution of sandstone unloading failure is studied, and the influences of the initial confining pressure and unloading rate on the deformation characteristics and permeability evolution of sandstone are discussed. It is found that the initial confining pressure and unloading rate have a significant impact on the axial and radial strains of the sandstone, and the radial deformation is more sensitive to the unloading process. The energy dissipation of the sandstone sample increases with the increase of the confining pressure and is nonlinearly correlated to the unloading time. The permeability during the unloading deformation of the sandstone decreases with the increase in the initial confining pressure, indicating that the initial confining pressure of unloading has an inhibitory effect on the permeability of the sandstone. The permeability decreases with the increase in unloading rate, indicating that the faster the increase in the unloading rate, the greater the dissipated instantaneous elastic energy, which is accumulated and released during failure. The higher the degree of fracture in the sample, the larger the peak value of permeability.

AbstractTo enrich understanding of how fluid flows through the natural filled jointed rock, the permeability experiments of the natural filled jointed dacite rock under repeated loading and unloading conditions are carried out. Experimental results show that the response of permeability of filled jointed dacite rock to confining pressure under repeated loading and unloading conditions is considered as porous media. Permeability and gas pressure are positively correlated under repeated loading and unloading conditions. Relationship between permeability and gas pressure shows that the higher confining pressure, the smaller permeability increasing rate. The calculated equivalent crack apertures are very small at the order of magnitude about 10−8m. The power law is found to well describe the relationship between equivalent crack aperture and effective stress of natural filled jointed dacite rock. With the parameters determined based on experiment results, an empirical expression is proposed.

AbstractThe triaxial compression tests under different hydraulic pressures and low confining pressure were carried out to study the hydraulic pressure effect on the mechanical properties and permeabilities of layered rock mass. In general, the results show that the increase of hydraulic pressure reduces the peak strength of layered rock mass and the layer structure changes the mechanical properties of rock mass. With the increase of hydraulic pressure, the peak strength of layered rock mass decreases sharply and then slows down. The peak permeabilities of layered rock mass occurred later than the time when the strength come to the peak. The increase of hydraulic pressure will accelerate the propagation of cracks and significantly enhance the peak permeability of layered rock mass. With the increase of the layer number, the effect of hydraulic pressure becomes more distinct. The initial permeability and the peak permeability increase largely. The increase of hydraulic pressure promotes the expansion and penetration of cracks, so the rock mass develops from tensile failure to the composite failure forms including shear failure and interfacial shear slip failure.

AbstractDuring the reservoir filling of many high arch dams, it was observed that the valley slopes on both sides deformed towards the river. The measured value of valley narrowing lateral deformation (VNLD) reached 89.54 mm by October 2018. This rare phenomenon contradicts the traditional experience of dam engineering and lacks a convincing explanation. An arch dam project in China is selected as a case study to analyze the mechanism of the VNLD. Based on a comprehensive analysis of the geological background and the VNLD measurements, a numerical study is performed to identify the main factors that may contribute to the VNLD phenomenon. It is concluded that the time-dependent valley deformation is due to the rheological behaviour of rock mass with the change of hydrogeological conditions. The artesian water head in the deep-confined aquifer is increased and the mechanical parameters in fault and shear zones are weakened after reservoir filling, which is associated with the specific hydrogeological structures. The rise of artesian water head is the main induced factor that contributes to the uplift deformation, which is observed at the dam site, and can cause the VNLD in the horizontal direction. The simulated results considering these factors agreed well with the observed data.

AbstractA series of triaxial compression tests under confining pressures of 0, 3, 5, 10, 15 and 20 MPa and lateral extension tests under initial confining pressures of 10, 15 and 20 MPa are conducted. Compared to the test results under loading and unloading conditions, lateral deformation is more sensitive than the axial deformation under unloading process. According to the characteristic of volume strain, the stress–strain curves can be divided into five phases: initial nonlinear compaction, linear elastic, stable crack growth, unstable crack growth and post-peak stage. The experimental results show that dominant failure mode of both tests is shear failure. The failure mode becomes more complex at the confining pressure of 20 MPa in the lateral extension test, consisting of shear and tensile failures. Based on experimental results, a coupled elasto-plastic damage model is proposed using a nonlinear yield function in the p–qplane, using a non-associated plastic potential function in the irreversible thermodynamic framework. The isotropic damage evolution model is formulated with plastic coupling. The numerical simulation results using the coupled model agree well with the experimental data of the fine-grained sandstone, indicating that this model can describe the main mechanical behaviours of the rock under complex loading conditions.