How to obtain alert velocity thresholds for large rockslides

A reliable forecast of the failure stage of large rockslides is difficult, because of non-linear time dependency of displacements and seasonal effects. Aim of this paper is to suggest a practical method to prepare alert thresholds for large rockslides, assessing critical values of velocity for carrying out civil protection actions using monitoring data. Adopted data concern the 20 Mm³ Ruinon rockslide (Valfurva, Central Alps, Italy), still evolving and suitable to originate a fast moving rock avalanche. Multitemporal analysis of aerial photos, LIDAR-ALTM laser topography, field survey and geomechanical analyses allowed to infer the rockslide kinematics and better understand data provided by a monitoring network including distometers, extensometers, GPS benchmarks and inclinometers. The analysis of displacement and rainfall data over five years (1997–2001) allowed to recognise three different evolutionary patterns of displacements, showing a continuously increasing rate since 1997. Data representing large-scale behaviour of the rock mass were fitted by power-law curves, according to the “accelerating creep” model by Voight, in order to evaluate a suitable failure time. This was hampered by the large seasonal deviations, which can significantly delay the occurrence of failure. Data were fitted using the Voight’s equation, expressed in terms of displacement, through non-linear estimation techniques, in order to find values of the controlling parameters (A, α and t_f) suitable to represent the mechanical behaviour of the rock mass approaching the failure. This allowed to compute velocity–time theoretical curves and to define different velocity threshold values (pre-alert, alert and emergency) to be used for emergency management.     

Uncertainty assessment in quantitative rockfall risk assessment

This study shows a quantitative rockfall risk assessment (QRA) for a slope of the Feifeng Mountain (China), including an explicit assessment of the uncertainties. For rockfall risk analysis, the annual probability of occurrence, reach probability, temporal–spatial probability and vulnerability of tourists were calculated for both dry and rainy day conditions. The resulting individual risk for exposed people visiting the historical site can be considered as acceptable for all scenarios, whereas the overall societal risk lies within the as low as reasonably practicable (ALARP) zone and therefore requires some mitigation actions. For the explicit assessment of uncertainty, an error propagation technique (first-order second moment (FOSM)) was adopted, starting from expert knowledge heuristic estimations of the coefficient of variation for each component of the risk analysis procedure. As a result, coefficients of variation of the calculated risk were obtained, ranging from 48 to 132 %, thus demonstrating the importance of accounting for uncertainty in rockfall risk modelling. A multi-criteria methodology is also proposed for the assessment of the standard deviation of the parameters adopted for the stochastic rockfall run-out model.     

Assessment of rockfall susceptibility by integrating statistical and physically-based approaches

In Val di Fassa (Dolomites, Eastern Italian Alps) rockfalls constitute the most significant gravity-induced natural disaster that threatens both the inhabitants of the valley, who are few, and the thousands of tourists who populate the area in summer and winter.To assess rockfall susceptibility, we developed an integrated statistical and physically-based approach that aimed to predict both the susceptibility to onset and the probability that rockfalls will attain specific reaches. Through field checks and multi-temporal aerial photo-interpretation, we prepared a detailed inventory of both rockfall source areas and associated scree-slope deposits. Using an innovative technique based on GIS tools and a 3D rockfall simulation code, grid cells pertaining to the rockfall source-area polygons were classified as active or inactive, based on the state of activity of the associated scree-slope deposits. The simulation code allows one to link each source grid cell with scree deposit polygons by calculating the trajectory of each simulated launch of blocks. By means of discriminant analysis, we then identified the mix of environmental variables that best identifies grid cells with low or high susceptibility to rockfalls. Among these variables, structural setting, land use, and morphology were the most important factors that led to the initiation of rockfalls.We developed 3D simulation models of the runout distance, intensity and frequency of rockfalls, whose source grid cells corresponded either to the geomorphologically-defined source polygons (geomorphological scenario) or to study area grid cells with slope angle greater than an empirically-defined value of 37° (empirical scenario). For each scenario, we assigned to the source grid cells an either fixed or variable onset susceptibility; the latter was derived from the discriminant model group (active/inactive) membership probabilities.Comparison of these four models indicates that the geomorphological scenario with variable onset susceptibility appears to be the most realistic model. Nevertheless, political and legal issues seem to guide local administrators, who tend to select the more conservative empirically-based scenario as a land-planning tool.     

Tectonic vs. gravitational morphostructures in the central Eastern Alps (Italy): Constraints on the recent evolution of the mountain range

Deep-seated gravitational slope deformations (DSGSDs) influence landscape development in tectonically active mountain ranges. Nevertheless, the relationships among tectonics, DSGSDs, and topography are poorly known. In this paper, the distribution of DSGSDs and their relationships with tectonic structures and active processes, surface processes, and topography were investigated at different scales. Over 100 DSGSDs were mapped in a 5000 km² sector of the central Eastern Alps between the Valtellina, Engadine and Venosta valleys. Detailed lineament mapping was carried out by photo-interpretation in a smaller area (about 750 km²) including the upper Valtellina and Val Venosta. Fault populations were also analysed in the field and their mechanisms unravelled, allowing to identify different structural stages, the youngest being consistent with the regional pattern of the ongoing crustal deformation. Finally, four DSGSD examples have been investigated in detail by geological and 2D geomechanical modelling.DSGSDs affect more than 10% of the study area, and mainly cluster in areas where anisotropic fractured rock mass and high local relief occur. Their onset and development is subjected to a strong passive control by mesoscopic and major tectonic features, including regional nappe boundaries as well as NW–SE, N–S and NE–SW trending recent brittle structures. The kinematic consistency between these structures and the pattern of seismicity suggests that active tectonics may force DSGSDs, although field evidence and numerical models indicate slope debuttressing related to deglaciation as a primary triggering mechanism.     

Semi-automated regional classification of the style of activity of slow rock-slope deformations using PS InSAR and SqueeSAR velocity data

Large slow rock-slope deformations, including deep-seated gravitational slope deformations and large landslides, are widespread in alpine environments. They develop over thousands of years by progressive failure, resulting in slow movements that impact infrastructures and can eventually evolve into catastrophic rockslides. A robust characterization of their style of activity is thus required in a risk management perspective. We combine an original inventory of slow rock-slope deformations with different PS-InSAR and SqueeSAR datasets to develop a novel, semi-automated approach to characterize and classify 208 slow rock-slope deformations in Lombardia (Italian Central Alps) based on their displacement rate, kinematics, heterogeneity and morphometric expression. Through a peak analysis of displacement rate distributions, we characterize the segmentation of mapped landslides and highlight the occurrence of nested sectors with differential activity and displacement rates. Combining 2D decomposition of InSAR velocity vectors and machine learning classification, we develop an automatic approach to characterize the kinematics of each landslide. Then, we sequentially combine principal component and K-medoids cluster analyses to identify groups of slow rock-slope deformations with consistent styles of activity. Our methodology is readily applicable to different landslide datasets and provides an objective and cost-effective support to land planning and the prioritization of local-scale studies aimed at granting safety and infrastructure integrity.

     

Chasing a complete understanding of the triggering mechanisms of a large rapidly evolving rockslide

Rockslides in alpine areas can reach large volumes and, owing to their position along slopes, can either undergo large and rapid evolution originating large rock avalanches or can decelerate and stabilize. As a consequence, in particular when located within large deep-seated deformations, this type of instability requires accurate observation and monitoring. In this paper, the case study of the La Saxe rockslide (ca. 8 × 10⁶ m³), located within a deep-seated deformation, undergoing a major phase of acceleration in the last decade and exposing the valley bottom to a high risk, is discussed. To reach a more complete understanding of the process, in the last 3 years, an intense investigation program has been developed. Boreholes have been drilled, logged, and instrumented (open-pipe piezometers, borehole wire extensometers, inclinometric casings) to assess the landslide volume, the rate of displacement at depth, and the water pressure. Displacement monitoring has been undertaken with optical targets, a GPS network, a ground-based interferometer, and four differential multi-parametric borehole probes. A clear seasonal acceleration is observed related to snow melting periods. Deep displacements are clearly localized at specific depths. The analysis of the piezometric and snowmelt data and the calibration of a 1D block model allows the forecast of the expected displacements. To this purpose, a 1D pseudo-dynamic visco-plastic approach, based on Perzyna’s theory, has been developed. The viscous nucleus has been assumed to be bi-linear: in one case, irreversible deformations develop uniquely for positive yield function values; in a more general case, visco-plastic deformations develop even for negative values. The model has been calibrated and subsequently validated on a long temporal series of monitoring data, and it seems reliable for simulating the in situ data. A 3D simplified approach is suggested by subdividing the landslide mass into distinct interacting blocks.