Exploring the potential of smartphone photogrammetry for field measurement of joint roughness

An, Pengju; Yong, Rui; Song, Jiamin; Du, Shigui; Wang, Changshuo; Xu, Hanhua; Fang, Kun; Tong, Shuochao

doi:10.1016/j.measurement.2023.114055

Cited by 10 publications

(2 citation statements)

References 41 publications

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“…The ICP algorithm serves as a method for change detection by matching point clouds, utilizing the coordinates of two point clouds to create a cost function that looks for a rigid transformation to minimize its residual error (Feng et al, 2019;An et al, 2024). The accurate calculation between points in 3D space is accomplished by calculating the rotations and translations generated during the matching of point clouds.…”

Section: D Deformation Analysis Methodsmentioning

confidence: 99%

3D deformation analysis for earth dam monitoring based on terrestrial laser scanning (TLS) and the iterative closest point (ICP) algorithm

Ning,

Zhang,

Jiang

et al. 2024

Front. Earth Sci.

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Introduction: The conventional 3-D point cloud-based deformation analysis methods, such as the shortest distance (SD), cloud-to-cloud (C2C), and multiscale model-to-model cloud comparison (M3C2), essentially regard the closest distance between two periods of point cloud data as the deformation, rather than the true position of the same point in 3-D space before and after deformation.Methods: This paper proposes a method based on the ICP algorithm to calculate the differences between the chunked multi-period point clouds to recognizes the 3-D deformations.Results and discussion: The results show that the obtained results are very close to the GNSS data but with a much larger spatial monitoring range. The accuracy is higher than that of the SD method. Moreover, we analyze the statistical relationship between the point cloud block size and the deformation vector error and determine the optimal block size. The aim of this article is to optimize the deformation analysis method and improve its accuracy to provide techniques and ideas for the wider surface deformation monitoring research field. For instance, combining this method with data from contact methods constructs a 3D overall deformation model of the mountain, enabling real-time monitoring and early warning of debris flows.

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Section: D Deformation Analysis Methodsmentioning

confidence: 99%

3D deformation analysis for earth dam monitoring based on terrestrial laser scanning (TLS) and the iterative closest point (ICP) algorithm

Ning,

Zhang,

Jiang

et al. 2024

Front. Earth Sci.

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“…shear strength of rock joints, as a main determinant for the stability of rock masses, has always been a research hotspot in rock mechanics (Leichnitz, 1985;Jiang et al, 2006;Bahaaddini et al, 2013;Ban et al, 2020a). Numerous studies have found that joint roughness plays a crucial role in the shear strength of rock joints (Lee et al, 2001;Tang et al, 2012;Song et al, 2023;An et al, 2024). In 1973, a peak shear strength estimation model of rock joints which intuitively reflected the significant influence of joint roughness on peak shear strength was proposed by Barton (1973).…”

mentioning

confidence: 99%

New 2D roughness parameters with geometric and physical meanings for rock joints and their correlation with joint roughness coefficient

He,

Tang,

Fang

et al. 2024

Front. Earth Sci.

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Determining the joint roughness accurately will better serve the peak shear strength estimation models of rock joints used for stability assessment of rock masses. Considering the defects of the existing quantitative characterization parameters for two-dimensional (2D) joint roughness, especially the lack of explicit geometric and physical meaning, we proposed two new 2D roughness parameters, θ2D and h2D. The former, θ2D, represents the average inclination angle of all potential contact asperities over the entire joint profile, while the latter, h2D, characterizes their average undulation height. Both parameters are closely related to the shear strength of rock joints. Then, the roughness parameters θ2D and h2D of 102 rock joint profiles digitized at 0.5 mm sampling interval were calculated, and a new nonlinear regression equation for the determination of the 2D joint roughness coefficient (JRC) was established by combining the calculated results of the two roughness parameters. It was verified that the proposed equation could give accurate JRC estimation values of the 10 standard profiles of rock joints. Through the comparative analysis of the experimental data collected from earlier studies for the peak shear strength of 73 rock joint samples and corresponding estimated values, the equation was further verified to be applicable and accurate for estimating the JRC values of rock joints. Furthermore, we discussed the effects of shear direction and sampling interval on roughness and further provided another equation that could be applied to estimate the JRC values of joint profiles at the sampling interval of 1.0 mm.

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High-resolution DEMs reveal multi-scale roughness of oyster reefs via smartphone photogrammetry

Zhuang,

Wang,

Guo

et al. 2024

Geo-Mar Lett

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Exploring the potential of smartphone photogrammetry for field measurement of joint roughness

Cited by 10 publications

References 41 publications

3D deformation analysis for earth dam monitoring based on terrestrial laser scanning (TLS) and the iterative closest point (ICP) algorithm

3D deformation analysis for earth dam monitoring based on terrestrial laser scanning (TLS) and the iterative closest point (ICP) algorithm

New 2D roughness parameters with geometric and physical meanings for rock joints and their correlation with joint roughness coefficient

High-resolution DEMs reveal multi-scale roughness of oyster reefs via smartphone photogrammetry

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