Výsledky projektu Chování zemin při teplotních změnách: geotechnická a teplotní analýza

Landslide susceptibility corresponds to the probability of landslide occurrence across a given geographic space.
This probability is usually estimated by using a binary classifier which is informed of landslide presence/absence
data and associated landscape characteristics. Here, we consider the Italian national landslide inventory to
prepare slope-unit based landslide susceptibility maps. These maps are prepared for the eight types of mass
movements existing in the inventory, (Complex, Deep Seated Gravitational Slope Deformation, Diffused Fall,
Fall, Rapid Flow, Shallow, Slow Flow, Translational) and we build one susceptibility map for each type. The
analysis – carried out by using a Bayesian version of a Generalized Additive Model with a multiple intercept for
each Italian region – revealed that the inventory may have been compiled with different levels of detail. This
would be consistent with the dataset being assembled from twenty sub–inventories, each prepared by different
administrations of the Italian regions. As a result, this spatial heterogeneity may lead to biased national–scale
susceptibility maps. On the basis of these considerations, we further analyzed the national database to confirm or
reject the varying quality hypothesis on the basis of the model equipped with multiple regional intercepts. For
each landslide type, we then tried to build unbiased susceptibility models by removing regions with a poor
landslide inventory from the calibration stage, and used them only as a prediction target of a simulation routine.
We analyzed the resulting eight maps finding out a congruent dominant pattern in the Alpine and Apennine
sectors.

To understand the factors that make certain areas especially prone to landslides, statistical
approaches are typically used. The interpretation of statistical results in areas characterised by complex
geological and geomorphological patterns can be challenging, and this makes the understanding
of the causes of landslides more difficult. In some cases, landslide inventories report information on
the state of activity of landslides, adding a temporal dimension that can be beneficial in the analysis.
Here, we used an inventory covering a portion of Northwestern Turkey to demonstrate that active
and relict landslides (that is, landslides that occurred in the past and are now stabilised) could be
related to different triggers. To do so, we built two landslide susceptibility models and observed that
the spatial patterns of susceptibility were completely distinct. We found that these patterns were
correlated with specific controlling factors, suggesting that active landslides are regulated by current
rainfalls while relict landslides may represent a signature of past earthquakes on the landscape. The
importance of this result resides in that we obtained it with a purely data-driven approach, and this
was possible because the active/relict landslide classification in the inventory was accurate.

Knowledge of physical and mechanical properties of geomaterials is fundamental to
characterise their response to external forcings (mechanical, climatic) at various scales. This is true,
for instance, in slope stability assessments, civil engineering works, and agriculture. The direct
evaluation of these properties in situ can be difficult, especially in inaccessible or vast areas, and so
can be the sampling and subsequent testing in the laboratory—where ensuring the
representativeness of the acquired data at the scale of analysis poses an additional challenge. Thus,
empirical correlations with more readily determinable quantities remain a powerful and practical
tool. Recently, several sensors, able to inform on various geomaterial properties, have been
developed. However, applications have typically targeted rocks, while studies on uncemented
geomaterials (soils, geotechnically speaking) are lacking. Here, we propose a simple method to
evaluate the porosity and critical state friction angle of soils via infrared thermography, consisting
of periodic acquisitions of images in infrared wavelengths. To demonstrate the method’s capability,
we analysed the cooling behaviour of samples of bentonite, kaolin, and sand (for which an extensive
characterisation exists in the literature), after compaction to different porosities and pre-heating in
an oven. We interpreted the results by seeking the optimal time interval for which a cooling rate
index (CRI) could be defined, which is best linked with the target property. We found that the CRI
correlates very well with the critical state friction angle (R2 > 0.85) and that different materials show
unique and strong (R2 = 0.86–0.99) relationships between their porosity and the CRI, which also
varies in a material-specific fashion according to the explored time interval.