Measurement of Rock Joint Surfaces by Using Smartphone Structure from Motion (SfM) Photogrammetry

An, Pengju; Fang, Kun; Jiang, Qiangqiang; Zhang, Haihua; Zhang, Yi

doi:10.3390/s21030922

Cited by 47 publications

(18 citation statements)

References 48 publications

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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%

“…[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]. The SfM is advantageous as it allows estimation of JRC error models for both laboratory and field photogrammetric surveys [41,42,46].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Torkan

Janiszewski

Uotinen

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Thus, the shear behavior of rock joints plays a key role in the safety and stability of underground mining [2][3][4][5][6]. The roughness of rock joints is recognized as one of the most important parameters affecting mechanical behavior [7][8][9][10][11]. Thus, quantitative characterization of discontinuity roughness has recently become a research hotspot in the field of rock mechanics.…”

Section: Introductionmentioning

confidence: 99%

A New Characterization Method for Rock Joint Roughness Considering the Mechanical Contribution of Each Asperity Order

Yuan

Luo

et al. 2021

Applied Sciences

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The morphology of the joint surface is multi-scale, and it can be divided into first-order asperity (waviness) and second-order asperity (unevenness). At present, the joint roughness characterization formula considers only the morphology contribution of waviness and unevenness components and does not fully consider their mechanical contribution. At same time, the relationship between the mechanical contribution and the morphology contribution is still unclear. Thus, the characterization formula considering the mechanical contribution of waviness and unevenness needs to be further studied. In this study, the standard joint roughness coefficient (JRC) profiles were first decomposed into waviness and unevenness. Then, three types of joint specimens with different asperity orders (flat, the standard JRC profile, and the profile containing only waviness) were prepared by the 3D engraving technique. Finally, direct shear tests were carried out on 39 sets of red sandstone joint specimens under three normal stresses. The mechanical contributions of waviness and unevenness were studied, the relationship between the mechanical contribution and the morphology contribution of waviness and unevenness was analyzed, and the characterization formula considering the mechanical contribution of waviness and unevenness was established. The results showed that the following: (1) the method combining the ensemble empirical mode decomposition (EEMD) and the critical decomposition level could be used to separate the waviness and unevenness from the joint surface; (2) the mechanical contribution of the waviness and unevenness decreased with the increase in normal stress; (3) the relationship between the mechanical contribution ratio and the statistical parameter ratio of the waviness and unevenness can be describe by power function; and (4) the roughness characterization formula considering the mechanical contribution and morphology contribution was established. This study will enhance the accurate evaluation of the roughness coefficient and shear strength of the joint specimen.

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“…Since the grain size are small, high quality and complete point cloud of which is needed. Point clouds obtained by binocular stereo vision, structure from motion and space carving are relatively sparse [15][16][17][18] , on the contrary the structured light imaging, an active three-dimensional vision technology, can obtain high-precision point clouds, which is widely used in industrial detection, reverse engineering and cultural relic protection 19 , and it provides an effective method for high precision analysis of cereal grain 3D traits.…”

Section: Introductionmentioning

confidence: 99%

Cereal Grain 3D Point Cloud Analysis Method For Shape Extraction And Filled/Unfilled Grain Identification Based On Structured Light Imaging

Qin

Zhong-fu

Hua

et al. 2021

Preprint

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Cereals are the main food for mankind. The grain shape extraction and filled/unfilled grain recognition are meaningful for crop breeding and genetic analysis. The conventional measuring method is mainly manual, which is inefficient, labor-intensive and subjective. Therefore, a novel method was proposed to extract the phenotypic traits of cereal grains based on point clouds. First, a structured light scanner was used to obtain the grains point cloud data. Then, the single grain segmentation was accomplished by image preprocessing, plane fitting, region growth clustering. The length, width, thickness, surface area and volume was calculated by the specified analysis algorithms for grain point cloud. To demonstrate this method, experimental materials included rice, wheat and corn were tested. Compared with manual measurement results, the average measurement error of grain length, width and thickness was 2.07%, 0.97%, 1.13%, and the average measurement efficiency was about 9.6 seconds per grain. In addition, the grain identification model was conducted with 25 grain phenotypic traits, using 6 machine learning methods. The results showed that the best accuracy for filled/unfilled grain classification was 90.184%.The accuracy for indica and japonica identification was about 99.785%,while for different varieties identification was only 47.252%. Therefore, this method was proved to be an efficient and effective way for crop research.

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Measurement of Rock Joint Surfaces by Using Smartphone Structure from Motion (SfM) Photogrammetry

Cited by 47 publications

References 48 publications

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

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

A New Characterization Method for Rock Joint Roughness Considering the Mechanical Contribution of Each Asperity Order

Cereal Grain 3D Point Cloud Analysis Method For Shape Extraction And Filled/Unfilled Grain Identification Based On Structured Light Imaging

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