The problem of cone penetration, particularly deep penetration, remains one of the most challenging in geotechnical engineering. It involves large displacements, rotations and deformation of soil elements in the path of the cone as well as complex response of the soil, including crushing and the development of large mean stresses, to the displacements imposed by the penetration process. As a result, rigorous theoretical solutions are not available for this problem, and experimental simulations of penetration provide insights that would not otherwise be available. This paper presents the results of a series of cone penetration tests performed in a half-circular chamber in sand samples prepared with three silica sands with different crushability. Cone resistance was measured, and digital images of the cone penetrating into the sand samples were acquired simultaneously during the entire penetration process. The digital image correlation (DIC) technique was then used to process these images to obtain the soil displacement field resulting from cone penetration. The results of DIC analyses and measured cone resistance suggest that the soil displacement around an advancing cone depends on the density and crushability of the sand, as well as the depth of penetration. Tests on silica sands with different degrees of crushability show that, for shallow penetration, the displacement vectors near the cone tip are essentially vertical for crushable sand, transitioning to subvertical for less crushable sands. However, for deep penetration, the displacement vectors near the cone tip are mostly vertical below the cone tip. Crushing was observed immediately below and around the cone tip for all sands tested. After passage of the cone, the crushed particles form a thin, crushed particle band of thickness equal to about 2 . 5D 50 along the shaft, with a smaller percentage of crushed particles observed within an outer band with thickness equal to 4D 50 .
Upon the introduction of machine learning (ML) and its variants, in the form that we know today, to the landslide community, many studies have been carried out to explore the usefulness of ML in landslide research and to look at some classic landslide problems from an ML point of view. ML techniques, including deep learning methods, are becoming popular to model complex landslide problems and are starting to demonstrate promising predictive performance compared to conventional methods. Almost all the studies published in the literature in recent years belong to one of the following three broad categories: landslide detection and mapping, landslide spatial forecasting in the form of susceptibility mapping, and landslide temporal forecasting. In this paper, we present a brief overview of ML techniques, provide a general summary of the landslide studies conducted, in recent years, in the three above-mentioned categories, and make an attempt to critically evaluate the use of ML methods to model landslide processes. The paper also provides suggestions for future use of these powerful data-driven techniques in landslide studies.
This paper presents the results of axial load tests performed on instrumented model piles pre-installed in a large-scale, half-circular chamber with a viewing window in its flat-side wall. Uniform silica sand samples were prepared with different densities using dry pluviation. The effects of pile surface roughness and soil density on the response of the soil during loading of the model piles were studied by analysing sequences of digital images using the digital image correlation technique. Test results show that the extent of the zone next to the pile that is affected by loading of the pile increases as the pile surface roughness and soil density increase. The development of a shear band next to the pile shaft was also studied by carefully analysing images taken with a digital microscope during loading of the model piles. The average thickness of the shear band is in the 3·2D50–4·2D50 range for rough model piles, whereas no shear band was observed for smooth model piles. Understanding of shear band formation along the pile–soil interface provides insights into the calculation of the shaft resistance of the pile as a function of initial soil density and stress as well as pile surface roughness.
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