A Blind Comparison Between Results of Four Image Analysis Systems Using a Photo-Library of Piles of Sieved Fragments

Latham, John; Kemeny, John G.; Maerz, Norbert H.; Noy, M.; Schleifer, Jacques; Tose, S.

doi:10.1076/frag.7.2.105.15899

Cited by 34 publications

(20 citation statements)

References 0 publications

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“…6). Errors of the A2-A3 samples in most of the volume calculations tend to be in the upper range, in agreement with the results by Latham et al (2003) on the trend of overprediction of x 50 by image analysis methods when it is small, which is the case for these two samples compared with the rest.…”

Section: Size Determinationsupporting

confidence: 78%

“…Latham et al (2003) check the consistency of four major image analysis systems, Fragscan, PowerSieve, Split and WipFrag, on artificially-built rock fragment samples of Rosin-Rammler type with uniformity indexes, n, ranging from 0.5 to 1.75 and median size, x 50 , between 15 and 52 mm. Most methods tend to over-predict x 50 for material with small x 50 and to under-predict x 50 for material with a large x 50 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On the accuracy of fragment size measurement by image analysis in combination with some distribution functions

et al. 2008

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SummarySize distributions of fragments of crushed rock in conveyor belts and of blasted rock in a muckpile obtained by sieving are compared with the size distributions obtained by digital image analysis of photographs of the same materials taken on-site. Several calculation methods are tested, based on the raw distribution of fragment areas and on the volume-transformed ones. The influence of the calibration of the system on the results and the performance of the system in a non-calibrated mode are evaluated. The capacity of some distributions (Rosin-Rammler, Swebrec and lognormal) to fit the data in the coarse region (where particles can be delineated, i.e. discriminated individually) and to extrapolate to the non-delineated fines (where particles cannot be outlined and their contour delineated) is assessed. The error between the sizes measured and the sizes of the reference distributions (determined by sieving) increases from the coarse to the fines region. The maximum error at a given size depends primarily on its value relative to the fines cut-off (FCO) of the image analysis. In general, at sizes greater than the FCO, where the system is able to delineate fragments reliably, both volume and surface-based, calibrated, calculations can determine the sizes with maximum error expectancy of about 30%. Below the FCO, only the calibrated, volume calculation maintains a maximum error of 30%, down to sizes of about one fourth the FCO, rapidly increasing for smaller sizes. Where the calibration is done based on data above the FCO, errors can be large below this point, in excess of 80% at sizes half the FCO. In the fines range (sizes smaller than 0.2 times the FCO) the maximum errors can be close to or greater than 100% for most of the calculations and function fittings. Of the distributions tested, all of them are acceptable at sizes above the FCO; below that, the Swebrec function seems to adapt better towards the fines than the Rosin-Rammler and lognormal.

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Section: Size Determinationsupporting

confidence: 78%

Section: Introductionmentioning

confidence: 99%

On the accuracy of fragment size measurement by image analysis in combination with some distribution functions

et al. 2008

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“…A previous paper (Kulatilake et al 2010) has covered the literature on the topic to an extensive level by referring to the following papers: Ghosh et al (1990), Mojtabai et al (1990), Ouchterlony et al (1990), Chakraborty et al (1994), Pal Roy (1995), Hagan (1995), Aler et al (1996), Ozcelik (1998), Jhanwar et al (2000, Castro et al (1998), Latham and Lu (1999), Hamdi and Du Mouza (2005), Hall and Brunton (2002), Latham et al (2003), Sanchidrian et al (2007, Gheibie et al (2009), andRustan (1998). Kuznetsov (1973) has suggested the following empirical equation to predict the mean fragmentation size resulting from rock blasting:…”

Section: Literature Reviewmentioning

confidence: 97%

New Prediction Models for Mean Particle Size in Rock Blast Fragmentation

Kulatilake

Hudaverdi

2012

Geotech Geol Eng

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The paper refers the reader to a blast data base developed in a previous study. The data base consists of blast design parameters, explosive parameters, modulus of elasticity and in situ block size. A hierarchical cluster analysis was used to separate the blast data into two different groups of similarity based on the intact rock stiffness. The group memberships were confirmed by the discriminant analysis. A part of this blast data was used to train a single-hidden layer back propagation neural network model to predict mean particle size resulting from blast fragmentation for each of the obtained similarity groups. The mean particle size was considered to be a function of seven independent parameters. An extensive analysis was performed to estimate the optimum value for the number of units for the hidden layer for each of the obtained similarity groups. The blast data that were not used for training were used to validate the trained neural network models. For the same two similarity groups, multivariate regression models were also developed to predict mean particle size. Capability of the developed neural network models as well as multivariate regression models was determined by comparing predictions with measured mean particle size values and predictions based on one of the most applied fragmentation prediction models appearing in the blasting literature. Prediction capability of the trained neural network models as well as multivariate regression models was found to be strong and better than the existing most applied fragmentation prediction model. Diversity of the blasts data used is one of the most important aspects of the developed models.

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“…Although some shortcomings exist, image analysis software has been the main tool used to measure the blast fragmentation during the last 10 years. The examination and the comparison of several image analysis software have been made by Latham et al 23. Image analysis software has been used by Sanchidrian et al 24 to measure the fragmentation of the quarry blasts.…”

Section: The Blasting Databasementioning

confidence: 99%

Prediction of blast fragmentation using multivariate analysis procedures

Hudaverdi

Kulatilake

Kuzu

2010

Num Anal Meth Geomechanics

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SUMMARYAn extensive multivariate analysis procedure for prediction of blast fragmentation distribution is presented. Several blasts performed in various mines and rock formations in the world are brought together and evaluated. Blast design parameters, the modulus of elasticity, in situ block size are considered to perform multivariate analysis. The hierarchical cluster analysis is used to separate the blasts data into different groups of similarity. Group memberships were checked by the discriminant analysis. The multivariate regression analysis was applied to develop prediction equations for the estimation of the mean particle size of muckpiles. Two different prediction equations were developed based on the rock stiffness. Validation of the proposed equations on various mines is presented and the capability of the prediction equations was compared with one of the most applied fragmentation distribution models appearing in the blasting literature. Prediction capability of the proposed models was found to be strong. Diversity of the blasts data used is one of the most important aspects of the developed models. The models are not complex and suitable for practical use at mines.

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A Blind Comparison Between Results of Four Image Analysis Systems Using a Photo-Library of Piles of Sieved Fragments

Cited by 34 publications

References 0 publications

On the accuracy of fragment size measurement by image analysis in combination with some distribution functions

On the accuracy of fragment size measurement by image analysis in combination with some distribution functions

New Prediction Models for Mean Particle Size in Rock Blast Fragmentation

Prediction of blast fragmentation using multivariate analysis procedures

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