Abstract

Accurate rock mass structural characterization is an important requirement to ensure safe and economical underground projects. Modern approaches have been developed to describe the rock mass fracture system and an effective modelling tool to this purpose is the Discrete Fracture Networks (DFN). DFN uses statistical distributions to generate realistic three-dimensional (3D) representations of a rock mass fracture network based on in-situ mapping. Deterministic data collection can be performed by employing several techniques, and this research makes use of laser scanning (i.e. Light Detection and Ranging - LiDAR) due to its easy integration with the excavation works and in order to investigate its effectiveness in collecting reliable data. Complimentary discontinuity data can be obtained from within the rock mass to add structural information to the DFN model and an optional technique to this intent is presented in this paper: the Distributed Optical Strain Sensing (DOS). DOS instrumented rock support is used to capture three-dimensional strain development at the support element and can be used to identify intersecting discontinuity planes. This ability is demonstrated in this paper through DOS instrumented double shear laboratory tests and an in-situ application exemplifies the rock mass movement monitoring through DOS technique during a tunnel excavation.

1 Introduction

Rock mass structural characterization is a fundamental requirement for underground excavation projects. Several engineering project decisions are based on this information in order to ensure safety during operations and throughout the structure useful lifetime. Accordingly, modern techniques are being developed to improve structural characterization and one of them is the Discrete Fracture Network (DFN), which has been successfully applied for underground projects. A DFN model is a 3D computer representation of the rock mass based on the geometrical features of discontinuities observed in-situ that are extrapolated through statistical or deterministic approaches (Fekete and Diederichs, 2012).

Regarding the input data for the DFN generation, mapping of the exposed rock mass regions can be conducted manually in loco, or virtually through laser scanning and photogrammetric techniques. The first part of this paper demonstrates how laser scanning (i.e. Light Detection and Ranging - LiDAR) can be used as part of a methodology to generate accurate DFN models. Accordingly, LiDAR, was used to scan the excavated area of a case study tunnel in order to generate 3D surface models. Through these models, the discontinuity orientation, length and density were determined and used as input parameters for the DFN generation. The case study herein presented is the Brockville Railway Tunnel.

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