A New Representative Sampling Method for Series Size Rock Joint Surfaces

Huang, Man; Hong, Chenjie; Luo, Zhiwei; Du, Shigui; Yang, Fei

doi:10.1038/s41598-020-66047-0

Cited by 11 publications

(3 citation statements)

References 25 publications

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“…It has been widely documented that sampling interval/solution affects the surface roughness characterization 25 , 43 . Figure 11 shows the percent errors of fractal dimensions of waviness and unevenness of the three samples at sample sizes from 200 mm × 200 mm to 1000 mm × 1000 mm at three resolutions.…”

Section: Results and Analysismentioning

confidence: 99%

Unveiling the mystery of scale dependence of surface roughness of natural rock joints

Yang

Sun

2022

Sci Rep

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Scale dependence of surface roughness of natural rock joints has long been an outstanding issue in rock mechanics. Controversial results were reported by various studies; however, the nature of scale dependency and the underlying mechanism for the conflicting observations remain unclear. Rock joints at different scales characterise two-order asperities, namely, waviness and unevenness; thus understanding how the individual roughness of waviness and unevenness vary as the joint size increases from the laboratory-scale to the large-scale is crucial for revealing the scale effect mystery. Here we digitise three natural granite joint surfaces with the same dimension of 1000 mm × 1000 mm through a high-resolution, three-dimensional scanner. Waviness and unevenness of each rock joint surface are quantitatively separated by selecting an appropriate sampling interval. The respective fractal dimensions of waviness and unevenness of joint surfaces sized from 100 mm × 100 mm to 1000 mm × 1000 mm are estimated through an improved roughness-length method. We find that the fractal dimensions of two-order roughness are scale-dependent but without generalised trends. The stationarity threshold beyond which the scale-dependency of roughness vanishes is absent for all the three joint samples, suggesting that the roughness of natural rock joints be assessed at the specific scale of the rock mass in-situ. We reveal that previous controversial results regarding scale effect are likely due to the composition of the roughness scaling of waviness and unevenness. Thus, accurate stability analysis of rock-engineering projects should consider separate contributions of multi-order asperities across scales to the strength and deformation of jointed rock masses.

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Section: Results and Analysismentioning

confidence: 99%

Unveiling the mystery of scale dependence of surface roughness of natural rock joints

Yang

Sun

2022

Sci Rep

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“…The possibility of achieving an intelligent sampling approach is indicated by sample results that meet the test accuracy standards. The findings of the comparison with the classic stratified sampling method show that the proposed method is more stable [16].…”

Section: Introductionmentioning

confidence: 98%

Clustering Analysis Using K-Medoids on Poverty Level Problems in Central Java by District/City

Sari¹,

Ediwijojo²

2023

KSS

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Poverty describes the absence of property and poor income or the circumstance that the food, shelter, and clothing needs cannot be met. The performance of Central Java in poverty reduction has risen and declined during the last decade. This research aims to perform mapping analyses utilizing artificial intelligence techniques as clusters on the number of poverty levels in Central Java districts or cities. Since Central Java is after West Java and East Java is the third most populous province, this was necessary to achieve in the last few years through regional mapping of a macro-picture of the poverty level. The dataset used is from the statistics agency website on the number of poor people (millennia) in 2017–2019. The data used are from the Central Java Statistical Agency. The way to map the clusters is using the k-medoids method which is part of data clustering. The number of clusters utilized for mapping poverty levels is high and low. The results showed six provinces (17%) in the high and 29 (83%) in the low. In the high cluster (cluster 1) and in the low cluster (cluster 1) and {18.6, 19.4, 20.1} the final centroid values for each cluster were {293.2, 309.2, 343.5}. The results of mapping can help address the poor in places in which the high cluster (cluster 1), Cilacap District, Banyumas District, Kebumen District, Grobogan District, Pemalang District, and Brebes District are a priority of the government in Central Java province. Keywords: K-Medoids, clustering, poverty

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“…In studies related to geomechanics, knowing the basic properties of rocks, such as their mechanical resistance, hydraulic flow, and fluid percolation, is essential to understanding their behavior in critical situations 1 , 2 . In this sense, the areas that present these properties are the fractures (joints), defined by the relationship between two rock surfaces separated at some points but in contact at others 2 , 3 . The anisotropic morphology of natural rock mass joints constantly affects their shear behavior 4 , 5 .…”

Section: Introductionmentioning

confidence: 99%

Determination of roughness coefficient in 3D digital representations of rocks

Scalco

Tonietto

Velloso

et al. 2022

Sci Rep

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The roughness property of rocks is significant in engineering studies due to their mechanical and hydraulic performance and the possibility of quantifying flow velocity and predicting the performance of wells and rock mass structures. However, the study of roughness in rocks is usually carried out through 2D linear measurements (through mechanical profilometer equipment), obtaining a coefficient that may not represent the entire rock surface. Thus, based on the hypothesis that it is possible to quantify the roughness coefficient in rock plugs reconstructed three-dimensionally by the computer vision technique, this research aims to an alternative method to determine the roughness coefficient in rock plugs. The point cloud generated from the 3D model of the photogrammetry process was used to measure the distance between each point and a calculated fit plane over the entire rock surface. The roughness was quantified using roughness parameters ($$R_a$$ R a ) calculated in hierarchically organized regions. In this hierarchical division, the greater the quantity of division analyzed, the greater the detail of the roughness. The main results show that obtaining the roughness coefficient over the entire surface of the three-dimensional model has peculiarities that would not be observed in the two-dimensional reading. From the 2D measurements, mean roughness values ($$R_a$$ R a ) of $$0.35\,\upmu \hbox {m}$$ 0.35 μ m and $$0.235\,\upmu \hbox {m}$$ 0.235 μ m were obtained for samples 1 and 2, respectively. By the same method, the results of the $$R_a$$ R a coefficient applied three-dimensionally over the entire rocky surface were at most $$0.165\,\upmu \hbox {m}$$ 0.165 μ m and $$0.166\,\upmu \hbox {m}$$ 0.166 μ m , respectively, showing the difference in values along the surface and the importance of this approach.

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A New Representative Sampling Method for Series Size Rock Joint Surfaces

Cited by 11 publications

References 25 publications

Unveiling the mystery of scale dependence of surface roughness of natural rock joints

Unveiling the mystery of scale dependence of surface roughness of natural rock joints

Clustering Analysis Using K-Medoids on Poverty Level Problems in Central Java by District/City

Determination of roughness coefficient in 3D digital representations of rocks

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