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Understanding the dynamics of slope instabilities is critical to mitigate the associated hazards, but their direct observation is often difficult due to their remote locations and their spontaneous nature. Seismology allows us to get unique information on these events, including on their dynamics. However, the link between the properties of these events (mass and kinematics) and the seismic signals generated is still poorly understood. We conducted a controlled rockfall experiment in the Riou Bourdoux torrent (southern French Alps) to try to better decipher those links. We deployed a dense seismic network and inferred the dynamics of the block from the reconstruction of the 3D trajectory from terrestrial and airborne high-resolution stereophotogrammetry. We propose a new approach based on machine learning to predict the mass and the velocity of each block. Our results show that we can predict those quantities with average errors of approximately 10 % for the velocity and 25 % for the mass. These accuracies are as good as or better than those obtained by other approaches, but our approach has the advantage in that it does not require the source to be localised, nor does it require a high-resolution velocity model or a strong assumption on the seismic wave attenuation model. Finally, the machine learning approach allows us to explore more widely the correlations between the features of the seismic signal generated by the rockfalls and their physical properties, and it might eventually lead to better constraints on the physical models in the future. [ABSTRACT FROM AUTHOR]

Over large regions exposed to natural disasters, cascading effects resulting from complex or concatenated natural processes may represent a large portion of total risk. Populated high‐mountain environments are a major concern, and methods for large‐scale quantitative risk analyses are urgently required to improve risk mitigation. This article presents a comprehensive quantitative rockfall risk assessment over a large archetypal valley of the Andean mountains, in Central Chile, which integrates a wide spectrum of elements at risk. Risk is expressed as an expected damage both in monetary terms and casualties, at different scales relevant for decision making. Notably, total rockfall risk is divided into its main drivers, which allows quantifying seismically induced rockfall risk. For this purpose, the local seismic hazard is quantified and the yield acceleration, that is, acceleration required to initiate rockfall, is determined at the regional scale. The probability of failure is thereafter derived in terms of annual frequency of rockfall initiation and integrated in the quantitative risk assessment (QRA) process. Our results show the significant role of seismic activity as the triggering mechanism of rockfalls, and highlight elements at risk that have a major contribution to the total risk. Eventually a sensitivity analysis is conducted to (i) assess the robustness of obtained risk estimates to the data and modeling choices and (ii) identify the most influential assumptions. Our approach evidences the feasibility of large‐scale QRAs in sensitive environments and opens perspectives for refining QRAs in similar territories significantly affected by cascading effects and multihazards. [ABSTRACT FROM AUTHOR]

In mountainous regions, global warming will likely affect the frequency and magnitude of geomorphic processes. This is also the case for rockfall, one of the most common mass movements on steep slopes. Rainfall, snowmelt, or freeze-thaw cycles are the main drivers of rockfall activity, rockfall hazards are thus generally thought to become more relevant in a context of climate change. At high elevations, unequivocal relationships have been found between increased rockfall activity, permafrost thawing and global warming. By contrast, below the permafrost limit, studies are scarcer. They mostly rely on short or incomplete rockfall records, and have so far failed to identify climatically induced trends in rockfall records. Here, using a dendrogeomorphic approach, we develop two continuous 60-year long chronologies of rockfall activity in the Vercors and Diois massifs (French Alps); both sites are located clearly below the permafrost limit. Uncertainties related to the decreasing number of trees available back in time were quantified based on a detailed mapping of trees covering the slope across time. Significant multiple regression models with reconstructed rockfalls as predictors and local changes in climatic conditions since 1959 extracted from the SAFRAN reanalysis dataset as predictants were fitted to investigate the potential impacts of global warming on rockfall activity at both sites. In the Vercors massif, the strong increase in reconstructed rockfall can be ascribed to the recolonization of the forest stand and the over-representation of young trees; changes that are observed should not therefore be ascribed to climatic fluctuations. In the Diois massif, we identify annual precipitation totals and mean temperatures as statistically significant drivers of rockfall activity but no significant increasing trend was identified in the reconstruction. All in all, despite the stringency of our approach, we cannot therefore confirm that rockfall hazard will increase as a result of global warming at our sites. [ABSTRACT FROM AUTHOR]

Understanding the dynamics of slope instabilities is critical to mitigate the associated hazards but their direct observation is often difficult due to their remote locations and their spontaneous nature. Seismology allows to get unique information on these events, including on their dynamics. However, the link between the properties of these events (mass and kinematics) and the seismic signals generated are still poorly understood. we conducted a controlled rockfall experiment in the Riou-Bourdoux torrent (South French Alps) to try to better decipher those links. We deployed a dense seismic network and inferred the dynamics of the block from the reconstruction of the 3D trajectory from terrestrial and airborne high-resolution stereo-photogrammetry. We propose a new approach based on machine learning to predict the mass and the velocity of each block. Our results show that we can predict those quantities with average errors of approximately 10 % for the velocity and 25 % for the mass. These accuracies are as good as or better than those obtained by other approaches, but our approach has the advantage of not requiring to localize the source and an a priori knowledge of the environment, nor of making a strong assumption on the seismic wave attenuation model. Finally, the machine learning approach allows us to explore more widely the correlations between the features of the seismic signal generated by the rockfalls and their physical properties, and might eventually lead to better constrain the physical models in the future. [ABSTRACT FROM AUTHOR]

At present, a quantitative basis for delimiting realistic rockfall runout zones on the basis of trajectory simulation data is generally missing. The objective of this study is to come up with standardized reach probability threshold values (RPTV) to separate "realistic" from "unrealistic" simulated rockfall runouts. We therefore compared reach probability values (Preach) simulated with Rockyfor3D for 458 mapped, fresh rockfall blocks (silent witnesses SW) on 18 different sites with a volume >= 0.05 m³ and estimated occurrence frequencies up to 300 years. We analysed which block, slope and forest characteristics influenced Preach of the SW based on a linear mixed effects model. The results indicate that the limit of a realistic runout zone lies in the range where simulated Preach values are between > 1 % and approximately 3 %. We conclude that RPTV can be defined to values lying in the range from 1.2 % to 2.5 % depending on the defined block volume and the encountered cumulative basal area in a forested transit zone. Where possible, the defined RPTV should be compared and validated by field recordings of SW. [ABSTRACT FROM AUTHOR]

Bending moment–curvature relations (M-χ) are useful in many beam modelling approaches. It allows describing in a simple way the mechanical behaviour at the cross-section scale and can be used to model continuous beams as single degree of freedom systems dedicated to perform stochastic analysis. This paper focused on tree mechanical charaterisation and presents an identification method for obtaining bending moment–curvature relationships up to failure from Digital Image Correlation (DIC) and experimental three points bending tests. Due to wood intrinsic variability, the proposed identification method has been validated from a Finite Element (FE) model which aims at simulating precisely the bending response of a cylindrical stem. The refined FE model has been developed based on the geometry and the boundary conditions of the experimentally tested stems. M-χ curves are derived from the FE model and used within a Single Degree Of Freedom (SDOF) approach. At the stem scale, the prediction of the SDOF model (Force-Displacement) is compared to the FE model and lead to a good agreement. Then the approach is used to characterise fresh European beech samples allowing deriving their mechanical properties (longitudinal Young modulus, modulus of elasticity and modulus of rupture). [ABSTRACT FROM AUTHOR]

Over large regions exposed to natural disasters, cascading effects resulting from complex or concatenated natural processes may represent a large portion of total risk. Populated high-mountain environments are a major concern, and methods for large-scale quantitative risk analyses are urgently required to improve risk mitigation. This article presents a comprehensive quantitative rockfall risk assessment over a large archetypal valley of the Andean mountains, in Central Chile, which integrates a wide spectrum of elements at risk. Risk is expressed as an expected damage both in monetary terms and casualties, at different scales relevant for decision making. Notably, total rockfall risk is divided into its main drivers, which allows quantifying seismically induced rockfall risk. For this purpose, the local seismic hazard is quantified and the yield acceleration, that is, acceleration required to initiate rockfall, is determined at the regional scale. The probability of failure is thereafter derived in terms of annual frequency of rockfall initiation and integrated in the quantitative risk assessment (QRA) process. Our results show the significant role of seismic activity as the triggering mechanism of rockfalls, and highlight elements at risk that have a major contribution to the total risk. Eventually a sensitivity analysis is conducted to (i) assess the robustness of obtained risk estimates to the data and modeling choices and (ii) identify the most influential assumptions. Our approach evidences the feasibility of large-scale QRAs in sensitive environments and opens perspectives for refining QRAs in similar territories significantly affected by cascading effects and multihazards., (© 2023 Society for Risk Analysis.)