Derivation of the Discontinuity Spacings Distribution from Frequency Data Along Scanlines and Boreholes

Stavropoulou, Maria; Saratsis, G.; Xiroudakis, George; Exadaktylos, G.

doi:10.1007/s00603-021-02442-x

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…Rock block is an important engineering parameter influencing the behavior of rock masses around underground openings and surface excavations, extraction of blocks of commercial sizes of dimension stones and in rock fragmentation processes by blasting or mechanical excavation techniques [1]. The combination of the aleatory nature of the rock mass structure and the epistemic errors related to the survey methods make rock mass characterization a challenge despite the remarkable evolution of the survey tools and the research on the subject.…”

Section: Introductionmentioning

confidence: 99%

Effect of uncertainties on block volume estimation

Carriero

Ferrero

Migliazza

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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The combination of the aleatory nature of the rock mass structure and the epistemic errors related to the survey methods make rock mass characterization a challenge despite the remarkable evolution of the survey tools and the research on the subject. In particular, significant uncertainties affect block volume estimation: the need for simplification connected to the engineering approach to rockfall problems, for instance, risks to mask the ripple effect of uncertainties on the reliability of the results. Even considering a simplified shape of the block created by three sets of discontinuities (i.e., a prism), the uncertainties on the geometrical characteristics of the discontinuities (orientation, spacing, and persistence) greatly influence the resulting volume distribution. It is a fact that a single value of the volume cannot be representative of the rock mass: the In Situ Block Size Distribution (IBSD) should be built to describe the variability of block volumes. Many statistical distribution functions can be used for fitting spacing data (i.e., gamma, negative exponential, log-normal, Weibull). The choice of the function must follow a rigorous evaluation of the goodness of fit. This research aims to assess the influence of the uncertainties related to the discontinuities sets, with particular reference to spacing samples, on block volume estimation. Through numerical examples and a case study, this research shows that a reduction of uncertainty can be reached by rigorous statistical processing of the data.

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Section: Introductionmentioning

confidence: 99%

Effect of uncertainties on block volume estimation

Carriero

Ferrero

Migliazza

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…However, post-processing of 3D point cloud data is too complicated and difficult, and it definitely increases the workload for the extraction of rock cracks. Although 3D crack trajectories containing real spatial information can be extracted from disordered point clouds, many researchers still use 2D data to quantify the parameter information of cracks on the rock surface [12,13]. Therefore, it is still reliable to quantify the crack width information based on 2D data.…”

Section: Introductionmentioning

confidence: 99%

Development and application of a more refined process for extracting rock crack width information based on artificial intelligence

Ye¹,

Yu²,

Zhang

et al. 2023

Preprint

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The process of image collection of high-altitude rock cracks using unmanned aerial vehicle (UAV) suffers from insufficient resolution and motion blur, which prevents more accurate detection of micro-cracks. Therefore, in this study, a rock crack refinement detection process (RC-RDP) based on super-resolution reconstruction (SRR) technique and semantic segmentation (SS) network is developed to detect micro-cracks. Four SRR networks (RCAN, SRDenseNet, ESRGAN, BSRGAN) and six SS networks (PSPNet, SegNet, DeepLab V3+, UNet++, UNet++(CBAM), SegFormer) are trained and tested separately using rock crack datasets. SRR indicators (PSNR and SSIM) and SS indicators (Precision, Recall, F1-Score and IoU) are applied to evaluate the performance of SRR networks and SS networks, respectively. According to the evaluation indicators of each network performance, in this paper, the RCAN network (PSNR = 31.08 dB and SSIM = 88.56%) is applied in the SRR part, and the UNet++ (CBAM) network is used in the crack detection part (Precision = 0.874, Recall = 0.886, F1-Score = 0.879 and IoU = 0.785). In addition, a novel data acquisition process integrating skeletonization algorithm, feature nodes detection algorithm, normal vector estimation algorithm and width extraction algorithm is used to calculate the minimum width WMin, maximum width WMax and average width WA information of the crack traces. The results of this paper show that the application of RC-RDP based on deep learning can obtain better super resolution (SR) images and more accurate width information. The method in this study shows excellent performance in detecting rock cracks.

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