SNCF French National Railway Company

Saint-Denis-d'Oléron, France

SNCF French National Railway Company

Saint-Denis-d'Oléron, France

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Assali P.,SNCF French National Railway Company | Grussenmeyer P.,CNRS Computer Science and Engineering Laboratory | Villemin T.,University of Savoy | Pollet N.,SNCF French National Railway Company | Viguier F.,SNCF French National Railway Company
Computers and Geosciences | Year: 2016

Rock mass characterization is obviously a key element in rock fall hazard analysis. Managing risk and determining the most adapted reinforcement method require a proper understanding of the considered rock mass. Description of discontinuity sets is therefore a crucial first step in the reinforcement work design process. The on-field survey is then followed by a structural modeling in order to extrapolate the data collected at the rock surface to the inner part of the massif. Traditional compass survey and manual observations can be undoubtedly surpassed by dense 3D data such as LiDAR or photogrammetric point clouds. However, although the acquisition phase is quite fast and highly automated, managing, handling and exploiting such great amount of collected data is an arduous task and especially for non specialist users. In this study, we propose a combined approached using both 3D point clouds (from LiDAR or image matching) and 2D digital images, gathered into the concept of ''solid image''. This product is the connection between the advantages of classical true colors 2D digital images, accessibility and interpretability, and the particular strengths of dense 3D point clouds, i.e. geometrical completeness and accuracy. The solid image can be considered as the information support for carrying-out a digital survey at the surface of the outcrop without being affected by traditional deficiencies (lack of data and sampling difficulties due to inaccessible areas, safety risk in steep sectors, etc.). Computational tools presented in this paper have been implemented into one standalone software through a graphical user interface helping operators with the completion of a digital geostructural survey and analysis. 3D coordinates extraction, 3D distances and area measurement, planar best-fit for discontinuity orientation, directional roughness profiles, block size estimation, and other tools have been experimented on a calcareous quarry in the French Alps. © 2016 Elsevier Ltd.


Assali P.,SNCF French National Railway Company | Grussenmeyer P.,CNRS Computer Science and Engineering Laboratory | Villemin T.,University of Savoy | Pollet N.,SNCF French National Railway Company | Viguier F.,SNCF French National Railway Company
Journal of Structural Geology | Year: 2014

Rock face diagnosis is a monitoring operation that is used to optimize rock-risk treatment works in terms of ensuring that safety requirements are met at the lowest cost. Diagnoses require measuring the location and orientation of rock discontinuities at the surface of the rock mass. These measurements are then entered into a structural model that extrapolates the data collected at the surface to the inner part of the rock mass. Currently, most surveys are empirical and are carried out manually using a compass-clinometer. In addition, they tend to examine only the most highly fractured area of a rock face, even though safety considerations demand an exhaustive study of the whole face. These deficiencies can be overcome by using dense 3D measurement techniques such as terrestrial laser scanning and optical imaging to obtain a more complete 3D model and structural statement. Hence, we have developed a semi-automatic process that allows 3D models to be combined with the results of field surveys in order to provide more precise analyses of rock faces, for example, by classifying rock discontinuities into subsets according to their orientation. Further research is being carried out in order to combine 3D data and 2D digital images as a support for structural survey. Trials carried out in a limestone quarry in the French Alps allowed us to compare data sets obtained using manual surveying methods, the well-known laser scanning method and the lower-cost photogrammetric survey method. © 2014 Elsevier Ltd.

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