Acta Geotechnica最新文献

Landslides are common geohazards worldwide, resulting in significant losses to economies and human lives. Data-driven approaches, especially machine learning (ML) models, have been widely used recently for landslide susceptibility mapping (LSM) by extracting features from geospatial variables based on their contribution to landslide occurrences using known distributions of landslides as the training dataset. However, challenges remain in applying ML models for LSM models due to the scarcity and uneven spatial distribution of landslide data coupled with the spatial heterogeneity of hillslope conditions. Moreover, ML models developed with limited data often exhibit unexpected behaviors, resulting in poor interpretability and predictions that deviate from intuitive expectations and established domain knowledge. To overcome these challenges, this study proposes a physics-guided machine learning (PGML) framework that integrates landslide domain knowledge into ML models for LSM. The PGML framework was developed and assessed using a detailed debris flow inventory from a storm event in the Colorado Front Range. Based on the infinite slope model, the factor of safety for the study area was first determined and was subsequently used to constrain the prediction of ML models through a modified loss function and measure the physics consistency of model predictions. To evaluate the robustness and generalizability of the models, this study uses geographical sample selections for model performance evaluation, where ML models are trained and tested across heterogeneous ecoregions. The results of this study demonstrated the efficacy of both physics-based and data-driven methods in determining landslide susceptibility in the study area; however, pure data-driven ML models produced physically unrealistic results and poor generalization performance in new ecoregions. With the incorporation of physical constraints, the PGML model demonstrated notable enhancements in physics consistency and generalization capability, along with reduced model uncertainties across various ecoregions, surpassing the performance of benchmark ML models.

Granular crushing significantly changes mechanical behaviours, especially under elevated stress levels. Therefore, this study aims to develop a model to simulate the constitutive behaviours of granular materials at the crushing-dominant stage. Firstly, the contour of elastic potential energy is demonstrated and employed to derive the yield surface or function, acknowledging that the stored elastic energy dominates the breakage yield criterion. The versatility of the proposed yield function in accurately capturing the features of yield surfaces is verified with three cases, including Cam-clay models, test results, and an empirical yield function. Next, a hardening parameter, H, is formulated, considering the extent of crushing, B, and the void ratio, e, to reflect the expansion of the yield surface during hardening. The proposed simple hardening formulation favourably represents compression characteristics under elevated stress levels. Combining the above results of yield and hardening functions, a new elastic–plastic-crushing constitutive model is developed; the model’s capability to describe crushable granular material behaviours is validated against experimental counterparts.

The coupling effect between the spatial variability of slurry diffusion and the time-varying viscosity of quick-setting slurry is the time–space characteristics of slurry viscosity (TSCSV). The TSCSV is the governing factor for the complex and uncontrollable flow of slurry in fractures. In this regard, the flow pattern of quick-setting slurry is considered as a Bingham fluid with viscosity spatiotemporal characteristics. Using the fractal function with random parameters (W-M function), a single fracture with random fracture opening (RFO) is drawn, and a theoretical model on grouting diffusion in fractures is established considering the TSCSV. The RFO is corrected for head loss, and the spatiotemporal distribution equation of the RFO grouting pressure is derived. The relationship between grouting pressure, grouting time, and slurry diffusion distance is obtained. Additionally, the effects of the RFO, the fractal dimension of the fracture curve, the horizontal movement distance of the lower end face of the fracture, and the effects of viscosity and grouting rate on the viscosity and grouting pressure in the slurry diffusion space are discussed. Finally, by predefining the spatial distribution function of slurry viscosity for a single fracture in the numerical calculation model, a numerical simulation of random fracture distribution grouting considering viscosity spatiotemporal characteristics is achieved. The rationality of the model is validated through a comparison of theoretical analysis and numerical simulation, providing reference for the determination of grouting parameters in practical grouting projects.

A gradation-dependent hypoplastic model is developed to capture the mechanical behaviours of crushable sands. First, a gradation evolution law is proposed to describe the variation of the grain size distribution (GSD) and the development of the extent of crushing during the loading process. Second, the family of CSLs under different gradations is formulated. Combining the developed CSL function and GSD evolution leads to well-modelling the unique critical state features of crushable sands. Subsequently, critical state features are incorporated into a well-established hypoplastic framework, such that the gradation-dependent hypoplastic model for crushable sands is developed. The accuracy and efficiency of the developed hypoplastic model in capturing the mechanical behaviours of crushable sands are validated against experimental counterparts.

Pipe piles enhanced by cement-improved soil (hereinafter referred to as enhanced pipe piles) have excellent bearing capacity compared with traditional piles and are often used as the foundation of offshore wind turbines and coastal soft-soil embankment. This study aims to clarify and gain an insight into the lateral dynamic response of enhanced pipe piles in unsaturated soil by developing an analytical approach. In the proposed approach, the enhanced pipe pile divides into two parts: The first part is considered as a composite pile formed by a concrete pipe pile and a cement-soil mixing pile through high-strength bonding, and the second part formed by a concrete pipe pile and an unsaturated soil column. The lateral vibration behaviours of the enhanced pipe pile and the unsaturated soil resistance are deduced by the Euler–Bernoulli beam theory and the porous viscoelastic theory of three-phase mixture, respectively. The closed-form solutions for the horizontal, rocking and horizontal-rocking dynamic impedances at the pile head of enhanced pipe pile under horizontal dynamic loads have been determined and then validated by comparing with the existing results. Numerical discussions are finally conducted to analysis the influence of physical parameters of enhanced pipe pile and unsaturated soil on the three types of dynamic impedance at the pile head. The main findings can be summarized as: (a) For the cement-soil mixing pile, its length should not exceed half of the concrete pipe pile, its radius size should be moderate and its elastic modulus can be as large as possible; (b) the wall thickness and elastic modulus of the concrete pipe pile can be appropriately increased to make the enhanced pipe pile achieve better vibration resistance and (c) the increase of the soil saturation will reduce the anti-vibration ability of enhanced pipe piles.