Application of automated grain sizing technique (AGS) for bed load samples at Rasil River: A case study for supply limited channel

Sulaiman, Mohd Sofiyan; Sinnakaudan, Shanker Kumar; Ng, Set Foong; Strom, Kyle

doi:10.1016/j.catena.2014.05.013

Cited by 19 publications

(8 citation statements)

References 31 publications

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“…Using the automatic threshold for image binarization, the morphological closed operation and watershed algorithm were used to remove the grain texture and separate the particles. The particle-size distribution characteristics of river-bed gravel were analyzed by using the equivalent elliptical short axis as the particle-size [6]. Hamzeloo, E et al detected the image of the stacked ore with 5mm and above in diameter on the conveyor belt.…”

Section: Introductionmentioning

confidence: 99%

Detection of size of manufactured sand particles based on digital image processing

Yang

Fang

et al. 2018

PLoS ONE

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The size distribution of manufactured sand particles has a significant influence on the quality of concrete. To overcome the shortcomings of the traditional vibration-sieving method, a manufactured sand casting/dispersing system was developed, based on the characteristics of the sand particle contours (as determined by backlit image acquisition) and an extraction mechanism. Algorithms for eliminating particles from the image that had be repeatedly captured, as well as for identifying incomplete particles at the boundaries of the image, granular contour segmentation, and the determination of an equivalent particle size, are studied. The hardware and software for the image-based detection device were developed. A particle size repeatability experiment was carried out on the single-grade sands, grading the size fractions of the manufactured sand over a range of 0.6–4.75 mm. A method of particle-size correction is proposed to compensate for the difference in the results obtained by the image-based method and those obtained by the sieving method. The experimental results show that the maximum repeatability error of single-grade fractions is 3.46% and the grading size fraction is 0.51%. After the correction of the image method, the error between the grading size fractions obtained by the two methods was reduced from 7.22%, 6.10% and 5% to 1.47%, 1.65%, and 3.23%, respectively. The accuracy of the particle-size detection can thus satisfy real-world measuring requirements.

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Section: Introductionmentioning

confidence: 99%

Detection of size of manufactured sand particles based on digital image processing

Yang

Fang

et al. 2018

PLoS ONE

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“…Thereafter, semiautomatic or automatic procedures were developed to reduce the processing time. They were based either on oriented-object approaches (McEwan et al, 2000;Sime & Ferguson, 2003;Graham, Reid, and Rice, 2005a;Lucas & Strom, 1987;Detert & Weitbrecht, 2012;Strom, Kuhns, & Lucas, 2010;Sulaiman et al, 2014) or on statistical approaches (Buscombe, 2010, Buscombe, 2013Sime & Ferguson, 2003;Warrick et al, 2009). However, this kind of method is suitable only for studies where prompt GSD data are needed.…”

mentioning

confidence: 99%

Use of terrestrial photosieving and airborne topographic LiDAR to assess bed grain size in large rivers: a study on the Rhine River

Chardon

Schmitt

Piégay

et al. 2020

Earth Surf Processes Landf

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Most grain size monitoring is still being conducted by manual sampling in the field, which is time consuming and has low spatial representation. Due to new remote sensing methods, some limitations have been partly overcome, but methodological progress is still needed for large rivers as well as in underwater conditions. In this paper, we tested the reliability of two methods along the Old Rhine River (France/Germany) to estimate the grain size distribution (GSD) in above-water conditions: (i) a low-cost terrestrial photosieving method based on an automatic procedure using Digital Grain Size (DGS) software and (ii) an airborne LiDAR topobathymetric survey. We also tested the ability of terrestrial photosieving to estimate the GSD in underwater conditions. Field pebble counts were performed to compare and calibrate both methods. The results showed that the automatic procedure of 1 2 terrestrial photosieving is a reliable method to estimate the GSD of sediment patches in both above-water and underwater conditions with clean substrates. Sensitivity analyses showed that environmental conditions, including solar lighting conditions and petrographic variability, significantly influence the GSD from the automatic procedure in above-water conditions. The presence of biofilm in underwater conditions significantly altered the GSD estimation using the automatic procedure, but the proposed manual procedure overcame this problem. The airborne LiDAR topographic survey is an accurate method to estimate the GSD of above-water bedforms and is able to generate grain size maps. The combination of terrestrial photosieving and airborne topographic LiDAR methods is adapted to assess the GSD along large rivers in entire sections that are several kilometers long.

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“…Zato se je v zadnjih letih povečala uporaba različnih alternativnih metod (npr. Sulaiman et al, 2014;Cislaghi et al, 2016); eden izmed teh je določanje zrnavosti plavin na podlagi analize slik, angl. »photo sieving«.…”

Section: Uvodunclassified

Optical granulometry of coarse aggregates and sediments

2019

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In order to determine the granularity of the material, image analysis may be used instead of traditional methods such as sieving. Data on granulometry is important in the water management sector for several practical applications, such as calculation of sediment transport capacity in watercourses, design of hydraulic structures, or modeling of debris flows. The WipFrag and Basegrain programs were tested on the case study of the Belca rockfall and the Sava Dolinka River’s gravel bar near the village of Mojstrana. In both cases, multiple images were taken from different heights and sieve analysis was performed. The results of both programs were compared with the results of the sieving analysis. The results showed that WipFrag yielded more comparable results with the sieving analysis than Basegrain. WipFrag gave slightly better results in the case of the Belca rockfall than in the case of the river gravel bar. The Basegrain program, on the other hand, produced similarly comparable results in both case studies. In most cases, both programs underestimated the grain size compared to the results of the sieving analysis. In some cases, the relative differences were close to 100%. On the other hand, the selected statistical test did not show a statistically significant difference between the results of the sieving analysis and the results of the image analysis in both of the programs. Optimal image analysis results were obtained from images taken from the height of about 1.5-2 m, which means that we suggest that photos of aggregates, fluvial sediments, and erosion material should be taken from a height of approximately ten times the maximum grain size.

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Application of automated grain sizing technique (AGS) for bed load samples at Rasil River: A case study for supply limited channel

Cited by 19 publications

References 31 publications

Detection of size of manufactured sand particles based on digital image processing

Detection of size of manufactured sand particles based on digital image processing

Use of terrestrial photosieving and airborne topographic LiDAR to assess bed grain size in large rivers: a study on the Rhine River

Optical granulometry of coarse aggregates and sediments

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