On the usability of different optical measuring techniques for joint roughness evaluation

Marsch, Kristofer; Wujanz, Daniel; Fernández-Steeger, Tomás Manuel

doi:10.1007/s10064-019-01606-y

Cited by 11 publications

(7 citation statements)

References 51 publications

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“…11 Influence of the input data in the power spectrum method for the type-profiles (simple detrending) a standard in the industry. According to Marsch et al (2020), consequently, the surface models produced with this procedure are trustworthy and useful in rock roughness evaluation.…”

Section: Data Acquisition and Handlingmentioning

confidence: 99%

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Comparative Evaluation of Statistical and Fractal Approaches for JRC Calculation Based on a Large Dataset of Natural Rock Traces

Marsch

Fernández-Steeger

2021

Rock Mech Rock Eng

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After the publication of the type-profiles for the estimation of the joint roughness coefficient (JRC) a discussion evolved about how to adequately use these traces. Based on the chart numerous researchers assembled mathematical correlations with various parameters seeking objectivity in the determination of JRC. Within these works differences concerning the database and the mathematical implementations exist. Consequently, each correlation, although predominantly the same parameters are used, leads to different JRC values. In theory, for any arbitrary profile, irrespective of the particular calculation approach, the same JRC should result. This is a requisite because of the referencing of all correlations to the 10 type-profiles. However, it is shown in this study that in most cases equal or even satisfactorily similar results are not obtained. The discrepancies are vast when non-standard profiles are evaluated, in this case, more than 40,000 traces from six different rock surfaces that cover a broad range of roughness categories. The simple intuitive parameter Z2 served as an agent for the statistical methods because of its broad use and consequently good comparability. On the part of the fractal approaches, three definitions were used. However, JRC inferred from fractal correlations are very much dependent on the particular calculation routine. In fact, the theory of fractals is overly complex for the sparse and low-resolution type-profiles. In summary, fractal approaches do not produce safer or more reliable estimates of roughness compared to simple statistical means and using Z2 perfectly suffices to determine the class of JRC.

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Section: Data Acquisition and Handlingmentioning

confidence: 99%

“…Nevertheless, it is extremely important to compare the approaches for a larger dataset of profiles to evaluate their universal validity. Only Marsch et al (2020) investigated the problem rudimentary. They showed that JRC calculated from the statistical parameter Z 2 were not equal or even satisfactorily similar to JRC calculated e.g.…”

Section: Introductionmentioning

confidence: 99%

Comparative Evaluation of Statistical and Fractal Approaches for JRC Calculation Based on a Large Dataset of Natural Rock Traces

Marsch

Fernández-Steeger

2021

Rock Mech Rock Eng

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“…e normal vector is a three-dimensional parameter that represents a plane, which can be obtained in CloudCompare software. e roughness along a joint face in different azimuths may help characterize the roughness in a more realistic way, providing insight into the variation in shear strength along different azimuths [26,34,35].…”

Section: Joint Roughnessmentioning

confidence: 99%

Change Detection and Feature Extraction of Debris‐Flow Initiation by Rock‐Slope Failure Using Point Cloud Processing

Xiao-ying

Kemeny

et al. 2021

Advances in Civil Engineering

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Our understanding of debris-flow initiation by slope failure is restricted by the challenge of acquiring accurate geomorphic features of debris flows and the structural setting of the rock mass in the remote mountainous terrain. Point cloud data of debris flows in Sabino Canyon, Tucson, Arizona, July 2006, with initiation by joint-controlled rock slope were obtained using multitemporal LiDAR scanning. Topographic changes were detected by comparing historical LiDAR scanning data of this area since 2005 by adopting open-source CloudCompare software. The results showed persistent scour and erosion in the debris flows after 2006. Point cloud data of joint-controlled rock in the initiation zone were generated by the means of photogrammetry using Pix4D software. The joint planes, the dip direction and the dip value of the joint plane, the joint spacing, and the joint roughness were therefore acquired by point cloud processing. Our study contributes a foundation for analyzing the relationship between the rock features, the generation of slope failure, and the initiation of debris flows.

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“…For instance, SfM enables estimation of the surface roughness [31,32], and the effect of the type of cameras, sensors, and lenses on roughness measurements was investigated in refs. [33,42,51,[53][54][55]. The SfM method allows investigation of the reliability of JRC values with high-resolution images [34][35][36][37]51], and photogrammetric JRC estimation for the design and stability analysis of slopes [43,44].…”

Section: Introductionmentioning

confidence: 99%

“…Other applications of SfM related to rock fracture characterization are prediction of peak shear strength [47], using 3D models obtained by photogrammetry for simulation of fluid flow through a fracture [48], and validation of photogrammetry data with pull and push shear tests to predict friction angle [45,50]. The method was applied successfully for different materials [51][52][53], different sample size [53], and the quality of the reconstructed model was investigated by the angular distance between subsequent images [56]. This efficient and low-cost remote sensing technique generates high-resolution digital 3D models of rock surfaces from a set of images.…”

Section: Introductionmentioning

confidence: 99%

Photogrammetric Method to Determine Physical Aperture and Roughness of a Rock Fracture

Torkan

Janiszewski

Uotinen

et al. 2022

Sensors

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Rock discontinuities play an important role in the behavior of rock masses and have a high impact on their mechanical and hydrological properties, such as strength and permeability. The surfaces roughness and physical aperture of rock joints are vital characteristics in joint shear strength and fluid flow properties. This study presents a method to digitally measure the physical aperture of a rock fracture digitized using photogrammetry. A 50 cm × 50 cm rock sample of Kuru grey granite with a thoroughgoing fracture was digitized. The data was collected using a high-resolution digital camera and four low-cost cameras. The aperture and surface roughness were measured, and the influence of the camera type and 3D model rasterization on the measurement results was quantified. The results showed that low-cost cameras and smartphones can be used for generating 3D models for accurate measurement of physical aperture and roughness of rock fractures. However, the selection of appropriate rasterization grid interval plays a key role in accurate estimations. For measuring the physical aperture from the photogrammetric 3D models, reducing rasterization grid interval results in less scattered measurement results and a small rasterization grid interval of 0.1 mm is recommended. For roughness measurements, increasing the grid interval results in smaller measurement errors, and therefore a larger rasterization grid interval of 0.5 mm is recommended for high-resolution smartphones and 1 mm for other low-cost cameras.

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On the usability of different optical measuring techniques for joint roughness evaluation

Cited by 11 publications

References 51 publications

Comparative Evaluation of Statistical and Fractal Approaches for JRC Calculation Based on a Large Dataset of Natural Rock Traces

Comparative Evaluation of Statistical and Fractal Approaches for JRC Calculation Based on a Large Dataset of Natural Rock Traces

Change Detection and Feature Extraction of Debris‐Flow Initiation by Rock‐Slope Failure Using Point Cloud Processing

Photogrammetric Method to Determine Physical Aperture and Roughness of a Rock Fracture

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