Circular rings are used in various engineering structures because of their nonlinear properties. The use of large assemblies of steel rings in flexible ring nets is widespread in recent technologies of protection structures and their design requires a nonlinear and computationally efficient mechanical modeling. This paper presents an innovative discrete model of steel rings for application in flexible protection structures. The model aims at providing an effective mechanical response of the net at both the local ring scale and the global net scale. Two different uniaxial tension configurations are defined as reference loading cases and the nonlinear analytical response of the ring model to these loading cases is established. A multicriteria numerical method based on the Levenberg-Marquardt algorithm is developed to calibrate the model parameters from experimental data. An experimental campaign on steel wire spliced rings of different dimensions is conducted and permits the calibration and validation of the model.

La Revue Française de Géotechnique (RFG) est une publication scientifique parrainée par les comités français de mécanique des sols, de mécanique des roches, de géologie de l’ingénieur et des géosynthétiques

When rockfalls hit and damage linear stakes such as roads or railways, the access to critical infrastructures (hospitals, schools, factories …) might be disturbed or stopped. Rockfall risk management often involves building protective structures that are traditionally based on the intensive use of resources such as steel or concrete. However, these solutions are expensive, considering their construction and maintenance, and it is very difficult to protect long linear stakes. A more sustainable and effective risk management strategy could be to account for changes on rockfall activity related to weather conditions. By integrating sustainability principles, we can implement mitigation measures that are less resource-intensive and more adaptable to environmental changes. For instance, instead of solely relying on physical barriers, solutions could include measures such as restriction of access, monitoring and mobilization of emergency kits containing eco-friendly materials. A critical step in developing such a strategy is accurately predicting periods of increased rockfall activity according to meteorological triggers. In this paper, we test four machine learning models to predict rockfalls on the National Road 1 at La Réunion, a key road for the socio-economic life of the island. Rainfall and rockfall data are used as inputs of the predictive models. We show that a set of features derived from the rainfall and rockfall data can predict rockfall with performances very close and almost slightly better than the standard expert model used for operational management. Metrics describing the performance of these models are translated in operational terms, such as road safety or the duration of road closings and openings, providing actionable insights for sustainable risk management practices.

The C2R-IA project (www.c2r-ia.fr) is aimed to better account for the influence of weather conditions on the level of rockfall hazards and to anticipate temporary increases in hazard levels during storms and other specific weather conditions, in order to implement risk mitigation systems (access restrictions, monitoring, mobilization of emergency kits, predictive maintenance). To achieve this, a database of rockfall events is built to train AI predictive models of rockfalls based on weather conditions. One of the monitoring technologies used is a terrestrial laser scanner with a RIEGL VZ-2000i long range 3D laser scanning system. Lidar point clouds are thus used to provide at several time intervals the 3D surface of the study site: the Saint-Eynard cliff, located northeast of Grenoble in the french Alps. From the lidar point cloud series, the goal is to compare the clouds to detect changes and identify rockfall events (Manceau et al, EGU 2025, oral presentaion). For a large and rich database, it is important to achieve very precise alignement between lidar point clouds to detect the smallest possible changes in our point clouds series (small rockfall volumes).In this context, a basic ICP (Iterative Closest Point) alignement reveals artefacts that need to be treated in a special way to achieve high-precision alignement. Geometric distortions are thus observed within the point clouds in the form of vertical strips. This phenomenon occurs at two scales:- Low frequency: observations of decimetric to multi-decimetric jumps with strip widths ranging from 10 to 100 meters during acquisitions from a tripod, a flexible support.- High frequency: observations of centimetric jumps with narrower strip widths (ranging from one to several meters) during acquisitions from a rigid base (reinforced concrete post).Several hypotheses are put forward and tested to explain the existence of these strips: machine-related mechanical issues, independent or dependent on time, interaction between the ground, support, and machine, changes in atmospheric conditions during the acquisition period (lasting 40 minutes), the geometry of the cliff and its local orientation relative to the lidar's line of sight.A processing method is proposed to overcome these geometric distortions during acquisition and maintain a low detection threshold when comparing two point clouds: this involves a new strip-based alignment of the two clouds before change detection. The first step is the extraction of strips from the compared cloud, then an independent alignment of each strip to the reference cloud is performed using the ICP method. Finally, the aligned strips are merged to form the new compared cloud : we reach a detection threshold of less than 10 cm (i.e. 10-4 times the measurement distance) whereas 40 cm has been previously used on the same site in the literature.