Ioannis Vazaios

Integration of Lidar-Based Structural Input and Discrete Fracture Network Generation for Underground Applications

In this study the authors present an approach of establishing and validating discrete fracture networks (DFNs) for underground projects using LiDAR (Light Detection and Ranging) as the source data. With the use of LiDAR in geotechnical and geological engineering becoming increasingly popular, it is necessary to establish the interactive application of this technology with other tools. Such a tool is the generation of DFNs and their integration into the geomechanical design, with a specific focus on underground projects such as tunnels, caverns, repositories etc. This paper attempts to show an approach in which LiDAR data from the Brockville Tunnel, located in Ontario, Canada, is used as the source for the determination of input parameters of DFN modelling based on manual and automatic mapping techniques. Having determined a representative set of input parameters, a deterministic DFN model is created in order to calibrate other modelling parameters associated with the generation process, leading to the creation of multiple DFN models. By employing the representative elementary volume (REV) concept, these models are used in order to examine the effect of the different joint sets on the estimated REV, and to introduce an approach of determining the required number of DFN realizations and the size of the DFN models.

The Effect of Jointing in Massive Highly Interlocked Rockmasses Under High Stresses by Using a FDEM Approach

In deep underground mines and deep infrastructure tunnels, spalling and strain bursting are among the most common failure mechanisms observed and reported in massive rockmasses under high stresses. Therefore, the need to be able to estimate such conditions and counter them with an economic and effective design is rising. Part of the common practice is the use of computer packages involving numerical methods based on continuum approaches. However, the failure mechanisms involved and the rockmass response observed are often difficult to capture by employing such methods and usually discontinuum approaches are better suited for this task. Additionally, discrete structures observed within the rockmass in situ, such as joints and other discontinuities, can be explicitly incorporated into the numerical model in order to investigate their effect on the overall rockmass response during an underground excavation and adjust the design if necessary. In this study, the presence of joints and their effect on the response of a hard rockmass under a high stress regime during an excavation is examined by employing a FDEM (Finite-Discrete Element Method) approach. The setup of the numerical model is based on the URL (Underground Research Laboratory) Test Tunnel located in Pinawa, Manitoba, Canada and a Discrete Fracture Network (DFN) model is implemented in order to simulate the impact of joints on brittle failure. Numerical results show that the presence of joints increases the intensity and evolution of the damage during the excavation.

3D Modelling of the ancient underground quarries of the famous Parian marble in the Aegean Sea, Greece and assessment of their stability using LiDAR scanning

Laser scanning has proven useful in the stability assessment of underground openings. High accuracy points are used to generate 3D surface models to evaluate their stability by assessing rockmass structural features. A series of scans obtained from underground ancient quarries in Paros Island were processed. The structural analysis of the openings was complemented by field observations and measurements, serving as input parameters for numerical software used to evaluate the potential failure mechanisms and overall stability. Blocks with a higher risk of detaching are encountered mostly at tunnel portals where support is required to secure unstable blocks to preserve this geotope.

Detection of Rock Discontinuity Traces Using Terrestrial LiDAR Data and Space-Frequency Transforms

Part of the rockmass assessment and its application in numerical modelling, within the geotechnical engineering field, is acquiring information such as discontinuity number, density, intensity, size etc., which can be obtained by mapping fracture traces on exposed rockmass surfaces and processing of the recorded field data. Moving past from traditional mapping techniques in the field, fracture traces can be extracted from terrestrial light detection and ranging (LiDAR) point-clouds or LiDAR-derived surface models. However, similarly to field mapping, the extraction of fracture-traces is often done manually. This is an arduous and timely task in most cases. The automatic-detection of such traces is an emerging topic in geotechnical engineering; however, existing methods focus solely on the spatial domain. Space-frequency representations are ideal for detecting singularities due to their localization in space and frequency. Furthermore, they allow multiscale analysis, which is important for isolating LiDAR-data noise and weak traces in lower scales. In this study, three space-frequency transforms are evaluated, namely, (1) wavelet, (2) contourlet, and (3) shearlet. In addition, the well-known methods of Sobel, Prewitt, and Canny for edge detection are used for comparison purposes. The performance of the different edge-detection methods is tested using data collected from the Brockville Tunnel in Ontario, Canada. Numerical and visual assessment show that contourlets and shearlets achieve the highest agreement with manually-extracted traces that are used for validation. The two methods, along with minimal user interaction, can be used in order to increase the efficiency of rockmass mapping and geometric modelling in stability assessment of tunnels, mines, slopes, and related applications.