Quantifying Morphology of Sands Using 3D Imaging

Alshibli, Khalid A.; Druckrey, Andrew M.; Al-Raoush, Riyadh I.; Weiskittel, Taylor M.; Lavrik, Nickolay V.

doi:10.1061/(asce)mt.1943-5533.0001246

Cited by 126 publications

(66 citation statements)

References 29 publications

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“…Also, it should be noted that the sphericity is defined for 2D images and hence differs from that defined for 3D images (Fonseca et al, 2012); for the former the measured values are between 0 and 1, with the upper limit for an ideally spherical shape, whereas for the latter the measured values can be greater than 1 and this is somehow odd. A detailed discussion of the differences between the two measurements of sphericity is outside the scope of this study; readers may refer to Alshibi et al (2014) and Fonseca et al (2012) for relevant details.…”

Section: Particle Shape Measurementsmentioning

confidence: 99%

Exploring the relationship between critical state and particle shape for granular materials

Yang

Luo

2015

Journal of the Mechanics and Physics of Solids

315

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Section: Particle Shape Measurementsmentioning

confidence: 99%

Exploring the relationship between critical state and particle shape for granular materials

Yang

Luo

2015

Journal of the Mechanics and Physics of Solids

315

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“…with reduced a) is likely to fall through the same sieve aperture. For silica sand, Fonseca et al (2012) showed that the sieving measurements were closer to b, while Alshibli et al (2015) indicated a good agreement with a.…”

Section: Grain Sizementioning

confidence: 84%

“…This results in non-unique shape descriptors for a given grain, and significant differences between 2D and 3D shape indices have been previously reported (e.g. Fonseca et al, 2012;Alshibli et al, 2015). This paper presents for the first time in the literature a systematic and comprehensive 3D quantification of the morphology, including size and shape, and intra-granular void ratios of shelly carbonate sands.…”

Section: Introductionmentioning

confidence: 95%

“…More details of the derivation of R o can be referred to Welzl (1991). A similar definition was adopted by Alshibli et al (2015); in their definition, however, the referential sphere has a diameter equal to c, which can yield I S values greater than one. The advantage of the definition presented here is that the maximum theoretical value for sphericity is 1 (i.e.…”

Section: Shape Parametersmentioning

confidence: 99%

“…Alshibli & Alsaleh, 2004;Cho et al, 2006) and 3D indices (e.g. Hawkins, 1993;Fonseca et al, 2012;Alshibli et al, 2015). There is no real consensus on which formulation is more effective to describe sphericity.…”

Section: Shape Parametersmentioning

confidence: 99%

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Quantification of the morphology of shelly carbonate sands using 3D images

2018

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This is the published version of the paper.This version of the publication may differ from the final published version. Shelly carbonate sands proliferate in regions of the world where construction of offshore structures is in high demand. These structurally weak sands have high intra-granular voids and complex angular grain shapes. To improve the understanding of the mechanical properties of the material, a detailed morphological quantification is required. This paper presents a three-dimensional characterisation of the morphology of shelly carbonate sands based on analyses of X-ray computed tomography images. Two sands from the Persian Gulf with distinct grading were investigated. An adaptive watershed segmentation technique was developed to identify the individual grains for subsequent grain-scale analysis, which overcomes the challenges posed by the intricate microstructure of these sands. Non-invasive measurements of size, intra-granular void and various shape parameters were carried out, and statistical analyses were conducted, to characterise the grains. The results help to better understand the mechanisms of grain interlocking, and the role of grain angularity and intra-granular void ratio on the mechanical behaviour of shelly carbonate sands. Permanent repository link

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Three‐dimensional quantitative analysis on granular particle shape using convolutional neural network

Zhang

Yin

Jin

2021

Num Anal Meth Geomechanics

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To identify all desired shape parameters of granular particles with less computational cost, this study proposes a three-dimensional convolutional neural network (3D-CNN) based model. Datasets are made of 100 ballast and 100 Fujian sand particles, and the shape parameters (i.e., aspect ratio, roundness, sphericity, and convexity) obtained by conventional methods are used to label all particles. For the model training, by feeding the slice images of particles into the model, the contour of particles is automatically extracted, thereby the shape parameters can be learned by the model. Thereafter, the model is applied to predict shape parameters of new particles as model testing. All results indicate the model trained based on slice images cut from three orthogonal planes presents the highest prediction accuracy with an error of less than 10%. Meanwhile, the accuracy for concave and angular particles can be guaranteed. The rotationequivariant of the model is confirmed, in which the predicted values of shape parameters are roughly independent of orientations of the particle when cutting slice images. Superior to conventional methods, all desirable shape parameters can be obtained by one unified 3D-CNN model and its prediction is independent of particle complexity and the number of triangular facets, thus saving computation cost.

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Quantifying Morphology of Sands Using 3D Imaging

Cited by 126 publications

References 29 publications

Exploring the relationship between critical state and particle shape for granular materials

Exploring the relationship between critical state and particle shape for granular materials

Quantification of the morphology of shelly carbonate sands using 3D images

Three‐dimensional quantitative analysis on granular particle shape using convolutional neural network

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