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Landslides 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/cm3 besides, and more than 90% were moved at the top of the slope for the soil density equal to 3 g/cm3. [ABSTRACT FROM AUTHOR]

The 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. [ABSTRACT FROM AUTHOR]

When excavating the rock foundation of a hydropower station, it will be affected by the phenomenon of unloading and relaxation, which may increase the risk of stability of the dam foundation engineering system. The dam foundation of Baihetan Hydropower is a columnar jointed rock mass (CJRM), which presents strong brittleness and anisotropy compared to traditional dam foundation rocks. Therefore, this type of rock mass is prone to disturbance to the dam body, structure, etc. during excavation, so it is necessary to accurately evaluate the impact of dam foundation excavation. Establishing a rock mass creep models serve as an effective tool for evaluating such stability but often suffer from significant parameter uncertainty. Digital twin technology, a virtual model, is capable of real-time learning from actual monitoring data obtained from the physical entity to enhance the performance of the built-in mechanistic model. In this study, the researchers employ the classical Burgers constitutive equation as the theoretical framework and integrate it with an ensemble smoother with multiple data assimilation (ESMDA) method based on Bayesian principles, along with displacement monitoring data from the Baihetan Dam foundation, to construct a digital twin model. Within this framework, the researchers analyze the uncertainty of rheological parameters at various measurement points in the Baihetan Dam foundation. Subsequently, the most suitable rheological parameters are selected and incorporated into the constitutive model to obtain displacement estimates, which are then compared with on-site monitoring data. The results demonstrate that the proposed method effectively performs probabilistic parameter estimation and model prediction for rheological mechanics. This research integrates data-driven methods with mechanical principles, offering a reliable approach for assessing the uncertainty of unloading rheological parameters and displacement prediction in dam foundations, thereby providing essential support for the evaluation of excavation projects in the CJRM of the Baihetan Dam foundation. [ABSTRACT FROM AUTHOR]

Large deformations of soft rocks often occur during the excavation and support of the Chuxiong section tunnel in the Yunnan Central Diversion Project. The physical characteristics and creep mechanical properties of the red-bed soft rock were analyzed by combining engineering experience, laboratory tests, and scanning electron microscopy. Furthermore, the creep parameters of the red-bed soft rock were obtained. The issues related to large deformations of deeply buried tunnels with soft and weak rock masses were investigated. Based on construction engineering practice and numerical simulations, characteristics of extrusion damage of deeply buried tunnels with soft and weak rock masses were analyzed. Countermeasures were implemented to solve the problems of large deformations, which can be used as references for the construction of similar soft rock tunnels in the future. [ABSTRACT FROM AUTHOR]