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Erschienen in: Geotechnical and Geological Engineering 6/2020

01.07.2020 | Original Paper

Rock Mass Structural Characterization Through DFN–LiDAR–DOS Methodology

verfasst von: Nicholas Vlachopoulos, Ioannis Vazaios, Bradley Forbes, Carla Carrapatoso

Erschienen in: Geotechnical and Geological Engineering | Ausgabe 6/2020

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Abstract

Underground projects require a realistic understanding of geological, structural and mechanical characteristics of the rock mass. This includes a comprehensive rock mass characterization, assessment to all existing site information and an accurate geological model. However, due to the lack of truly three-dimensional subsurface fracture geometry data, this reality is rendered difficult. Thus, the use of Discrete Fracture Networks (DFNs) arises as an interesting tool. DFN models are statistically generated from discontinuity features mapped in situ. These deterministic data can be obtained by employing various techniques, and more specifically with the use of laser scanning (i.e. Light Detection and Ranging—LiDAR). Complimentary discontinuity data can be obtained through fiber optic sensors coupled to tendon rock support elements, adding structural information from within the rock mass to the DFN model. This paper presents the methodology associated with the implementation of LiDAR data into DFN models for the purpose of application in underground projects and the use of optical sensors to capture three-dimensional loading of tendon support and, in turn, identify intersecting discontinuity planes. The Distributed Optical Strain Sensing technology is demonstrated through double shear laboratory tests, as well as through an in situ application to monitor rock mass movement during a tunnel excavation.

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Metadaten
Titel
Rock Mass Structural Characterization Through DFN–LiDAR–DOS Methodology
verfasst von
Nicholas Vlachopoulos
Ioannis Vazaios
Bradley Forbes
Carla Carrapatoso
Publikationsdatum
01.07.2020
Verlag
Springer International Publishing
Erschienen in
Geotechnical and Geological Engineering / Ausgabe 6/2020
Print ISSN: 0960-3182
Elektronische ISSN: 1573-1529
DOI
https://doi.org/10.1007/s10706-020-01431-1

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