Correlation of the Rock Mass Rating (RMR) System with the Unified Soil Classification System (USCS): Introduction of the Weak Rock Mass Rating System (W-RMR)

Warren, S. N.; Kallu, Raj R.; Barnard, Chase K.

doi:10.1007/s00603-016-1090-1

Cited by 24 publications

(11 citation statements)

References 6 publications

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“…14, the calculated width information is a rather accurate estimation, it has the required practical accuracy to allow geologists to evaluate regional rock mass parameters. After all, most of the rock evaluation systems are based on relatively rough estimated ranges, and the evaluation of rock geometry parameters is only one of the many estimations [68,69]. In addition, it must be emphasized that the light and shadow still have a strong influence on the final detection results.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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 relationship between the RMR 76 indicators and Q can be derived from field data and statistical analysis [56][57][58], and can be expressed as…”

Section: Estimation Of Gsi Using Acoustic Testing Methodsmentioning

confidence: 99%

Research on a Non-Synchronous Coordinated Reduction Method for Slopes Based on the Hoek–Brown Criterion and Acoustic Testing Technology

He,

Zhao,

Yin

et al. 2023

Sustainability

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In the process of the evolution of rocky slope instability, the decay deterioration rate of cohesion c and internal friction angle φ are different, and there are also differences in the order and degree of their impact on slope stability; thus, it is of great theoretical value to propose a more practical calculation method for the reduction in slope degradation. This paper combines the Hoek–Brown criterion and an acoustic test method to estimate the mechanical parameters of slope rock mass; the correlative relationship within the double-strength parameter reduction was established by introducing advanced reduction steps (ARS), n, and correlation factor, λ, and a non-synchronous coordinated reduction (NSCR) method for the double parameters of slopes was proposed. Furthermore, methods for determining the comprehensive safety factor (CSF) of slopes during the coordinated reduction of double parameters are comparatively analyzed. The results of the application of engineering examples show that the strength of the slope rock mass is significantly reduced after several blast disturbances, and the equivalent cohesion is reduced from 1.05 MPa to 0.89 MPa, while the internal friction angle is reduced from 25.68° to 21.77°. The CSF calculated using the W. Yuan-2 method is closer to the results of the limit equilibrium method and is suitable for the calculation of the CSF of the NSCR of slopes. The slope CSFs show a trend of first increasing and then decreasing with the increase in n; FS = 3.349 when n = 50, with a relative error of only 8.1% compared to the results calculated using the limit equilibrium method. The NSCR method remediates the blindness of the traditional strength reduction method in double-parameter reduction and ensures that the reduction range of the internal friction angle is no lower than its residual strength limit value, making it practical and feasible for slope stability analysis.

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“…Additionally, all the measured MB i values are shown in figure 19. Based on figures 17–19, the ratings of RQD plus joint spacing ( R RQD+JS ) and MB i ( R M ) were calculated, according to [28] and equation (5.1), respectively, as shown in figure 20. This figure shows that for the majority of theoretical DFN models, the R RQD+JS and R M share similar evaluation results, e.g.…”

Section: A Rock Mass Quality Classification System: Rmrmbimentioning

confidence: 99%

Integration of homogeneous structural region identification and rock mass quality classification

Chen

Yin

2019

R. Soc. open sci.

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In rock engineering projects, professionals assess the overall rock mass qualities using a sole value. However, the true qualities of partial rock masses are incompatible with such a value. To address this problem, the idea of regionally classifying rock mass qualities is proposed and the associated procedure presented. To achieve this goal, the probabilistic and deterministic joints within the study area were determined, and a three-dimensional joint network model was created. Then, the three-dimensional joint network model was discretized into interlocking subdomains, and the modified blockiness index (MBi) was used to finely identify the homogeneous structural regions, together with the k-means algorithm and the sum of squared errors (SSE). A synthetic model comprising homogeneous structural regions was developed and validated with respect to the extracted cross-sections. Next, an improved rock mass rating system (RMRmbi) was introduced, and the viability of RMRmbi was supported through a significant amount of theoretical cases and several real cases. Finally, visualization of regional RMRmbi classification results was performed. Results show that: (i) the homogeneous structural regions are finely demarcated in three dimensions, and (ii) the proposed idea can overcome the problem of rock mass quality classification using the conventional approach often leading to ‘overgeneralization’.

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Correlation of the Rock Mass Rating (RMR) System with the Unified Soil Classification System (USCS): Introduction of the Weak Rock Mass Rating System (W-RMR)

Cited by 24 publications

References 6 publications

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

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

Research on a Non-Synchronous Coordinated Reduction Method for Slopes Based on the Hoek–Brown Criterion and Acoustic Testing Technology

Integration of homogeneous structural region identification and rock mass quality classification

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