Foto del docente

Liwei Hu

Dottorando

Dipartimento di Matematica

Tutor didattico

Dipartimento di Matematica

Settore scientifico disciplinare: MAT/08 ANALISI NUMERICA

Temi di ricerca

Parole chiave: Landslide Thickness Estimation Landslide Detection Inverse Problems in Engineering Deep Learning for Science

Landslide thickness estimation:

Landslides represent a significant natural hazard that threatens infrastructures, human lives, and property around the world (Froude and Petley, 2018). Understanding their behavior and potential impact requires an accurate characterization of their key physical properties.

Among these quantities, the thickness of the landslide, or the depth of the failure surface, plays a crucial role in this context. For example, if the thickness is known across the entire ground surface, from ground elevation data, the elevation of the failure surface can be obtained. Being able to assess the latter implies that the underground, otherwise hidden geometrical shape of the main body can be extrapolated, providing valuable yet traditionally inaccessible information to field experts. The shape, orientation, and depth of the
failure surface are important for stability analysis, as they may highlight horizons where groundwater pressures or shear strength parameters are critical, and these information are essential for stability analysis and for design of remedial works (Carter and Bentley, 1985).


Another critical quantity which can be derived from landslide thickness is the volume. The product of the surface area and the average depth of landslide is often used to estimate landslide volume (Guzzetti et al., 2009). Being volume one of the most important parameters controlling the distance of propagation,
the affected area, and potential damages, a more accurate assessment of volume is highly desirable in quantitative risk analysis (von Ruette et al., 2016). From measurements of surface area and landslide thickness, a more precise value of volume can be computed. Moreover, from a practical point of view, even a rough estimate of the thickness at a single point can be useful for borehole planning, and effective placing of piezometers, inclinometers, and other instrumentations (Hutchinson, 1983).


Three-dimensional failure surfaces may be used to guide regional slope stability analyses as well (Bunn et al., 2020). Estimated rupture surfaces cannot perfectly represent the subsurface geometry of a landslide, but they are capable of providing reasonable, first-order approximations. These approximations mean that processes developed for studies of individual landslides, such as back-analyses, may be implemented to a regional scale. Back-analyses are considered fundamental for detailed landslide studies, and the ability to implement similar analyses on regional scale would greatly expand the understanding of regional trends in mechanical properties of soil and rock, which are often poorly characterized spatially. Further applications include those listed in Hutchinson (1983), studies aimed at designing consistent early-warning systems and producing plausible susceptibility maps (Bunn et al., 2020).


In summary, the knowledge of the failure surface beneath a landslide is of great importance for many reasons, the most important ones include accurate stability analysis, design of effective remedial work, realistic risk assessment, efficient field survey planning, and landscape evolution understanding.

Ultimi avvisi

Al momento non sono presenti avvisi.