Continuous assessment of landslides by measuring their basal temperature

Abstract

In this study, we suggest a temperature-based assessment and mitigation approach for deep-seated landslides that allows to forecast the behavior of the slide and assess its stability. The suggested approach is validated through combined field monitoring and experimental testing of the El Forn landslide (Andorra), whose shear band material is Silurian shales. Thermal and rate controlled triaxial tests have shown that this material is thermal- and rate-sensitive, and in combination with the field data, they validate the theoretical assumption that by measuring the basal temperature of an active landslide, we can quantify and reduce the uncertainty of the model’s parameters, and adequately monitor and forecast the response of the selected deep-seated landslide. The data and results of this letter show that the presented model can give threshold values that can be used as an early-warning assessment and mitigation tool.

Fusing physics-based and data-driven models to forecast and mitigate landslide collapse

“…Moreover, other studies (20) show that adding internal factors in the model of a deep-seated landslide, such as material properties of the shear band, constrain triggering factors of the behavior of a deep-seated landslide and follow its accelerations during its creeping phase until its final collapse. Most deep-seated landslides present a shear band formed by clay or clay-like materials, which can be thermal and velocity-sensitive (24,25) when the material experiences changes in pressure and is sheared, thus facilitating the acceleration of the landslide.…”

“…The difficulty of adding too much information from the physical mechanisms operating in the landslide's shear band is how to evaluate these parameters in the unavailability to access shear band material. For this reason, we suggest a method of fusing a physics-based model with data-driven parameter constrain by using an energy-based mathematical model in a reduced and dimensionless form (that depends on a single dimensionless parameter that can be constrained by the data) (20,25). The model is described in the Supplementary Materials (Methods) and includes a single dimensionless parameter, the Gruntfest number (26) Gr, expressing the ratio of the mechanical work converted into heat over the heat diffusion capabilities of the shear-band material (Eq.…”

“…S1). The Gruntfest number includes all the material properties at hand (thermal conductivity, rate and thermal sensitivities, and reference rate) (24), as well as the thickness of the shear band and the loading shear stress (applied on the shear band from the external loading sources, such as gravity and groundwater) (20,25). In the absence of detailed information on the material, this dimensionless parameter can be used as a free parameter and be constrained by available data (25).…”

Subsurface Multi‐Physical Monitoring of a Reservoir Landslide With the Fiber‐Optic Nerve System

“…Part of the overburden layers around ∼20 m depth absorbs pore fluid derived from rainfall recharge, forming an unconfined seasonal aquifer. The shear band composed of clay‐like materials could act as an impermeable barrier that accumulates migrated pore fluid, thus resulting in the significant strength degradation of the geomaterials (Seguí & Veveakis, 2021). This is the reason why the temperature of the overburden layers decreased, especially near the depth of ∼20 m. When large shear deformation had occurred and the rainfall was less or lighter in mid‐late July, the moisture in the slip zone moved backward to the adjacent layers.…”

Application of Infrared Thermography for Estimating Residual Stress in Ground Anchors for Maintenance

“…Infrared thermography (IR) was initially used for military purposes (Gao et al 2017;Guerin et al 2019) and has been recently used in the engineering eld to determine the characteristics and defects of objects. Heat generated from an object depends on radiation, convection, and conduction, and among these, radiation has the greatest effect on the diffusion of thermal energy (Pappalardo et al 2018;Liu et al 2020;Seguí and Veveakis. 2021).…”