xP-frag, a distribution-free model to predict blast fragmentation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

xP-frag, a distribution-free model to predict blast fragmentation. / Sanchidrián, Jose A; Ouchterlony, Finn.
Proc 43rd ISEE Conference on Explosives and Blasting Technique. Vol. 43 Cleveland, OH, 2017. p. 265-280.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Sanchidrián, JA & Ouchterlony, F 2017, xP-frag, a distribution-free model to predict blast fragmentation. in Proc 43rd ISEE Conference on Explosives and Blasting Technique. vol. 43, Cleveland, OH, pp. 265-280.

APA

Sanchidrián, J. A., & Ouchterlony, F. (2017). xP-frag, a distribution-free model to predict blast fragmentation. In Proc 43rd ISEE Conference on Explosives and Blasting Technique (Vol. 43, pp. 265-280).

Vancouver

Sanchidrián JA, Ouchterlony F. xP-frag, a distribution-free model to predict blast fragmentation. In Proc 43rd ISEE Conference on Explosives and Blasting Technique. Vol. 43. Cleveland, OH. 2017. p. 265-280

Author

Sanchidrián, Jose A ; Ouchterlony, Finn. / xP-frag, a distribution-free model to predict blast fragmentation. Proc 43rd ISEE Conference on Explosives and Blasting Technique. Vol. 43 Cleveland, OH, 2017. pp. 265-280

Bibtex - Download

@inproceedings{54a4ad1e571b49bbbc41674f0b1e20fa,
title = "xP-frag, a distribution-free model to predict blast fragmentation",
abstract = "A model for fragmentation in bench blasting that originates from dimensional analysis of fragmentation in asteroid collisions is presented. Percentiles of the size distribution are obtained in the basic model as the product of a rock strength-to-explosive energy ratio, a bench shape factor, a scale factor or characteristic size and a percentage passing-related factor. The parameters of the model are fitted to 169 bench blasts in different sites and rock types, bench geometries and delay times, for which the fragmentation of the muckpile was obtained by sieving. The basic model is found to significantly improve with an additional factor describing the rock mass structure in terms of the spacing andorientation of discontinuities, and another one describing the delay between successive contiguous shots; the latter is conveniently formulated as a function of the P-wave velocity and the holes spacing. The rock strength property chosen is the strain energy at rupture that, together with the explosive energy density in the rock (or energy powder factor), forms a combined rock strength/explosive energy nondimensionalfactor. The model, called xP-frag, is applicable from 5 to 100 percentile fragment sizes, with all parameters determined from the fits significant to a 0.05 level. The expected error of the prediction is below 25 % at any percentile. These errors are half to one third of the errors expected with the best prediction models available to date.",
author = "Sanchidri{\'a}n, {Jose A} and Finn Ouchterlony",
year = "2017",
month = jan,
day = "29",
language = "English",
volume = "43",
pages = "265--280",
booktitle = "Proc 43rd ISEE Conference on Explosives and Blasting Technique",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - xP-frag, a distribution-free model to predict blast fragmentation

AU - Sanchidrián, Jose A

AU - Ouchterlony, Finn

PY - 2017/1/29

Y1 - 2017/1/29

N2 - A model for fragmentation in bench blasting that originates from dimensional analysis of fragmentation in asteroid collisions is presented. Percentiles of the size distribution are obtained in the basic model as the product of a rock strength-to-explosive energy ratio, a bench shape factor, a scale factor or characteristic size and a percentage passing-related factor. The parameters of the model are fitted to 169 bench blasts in different sites and rock types, bench geometries and delay times, for which the fragmentation of the muckpile was obtained by sieving. The basic model is found to significantly improve with an additional factor describing the rock mass structure in terms of the spacing andorientation of discontinuities, and another one describing the delay between successive contiguous shots; the latter is conveniently formulated as a function of the P-wave velocity and the holes spacing. The rock strength property chosen is the strain energy at rupture that, together with the explosive energy density in the rock (or energy powder factor), forms a combined rock strength/explosive energy nondimensionalfactor. The model, called xP-frag, is applicable from 5 to 100 percentile fragment sizes, with all parameters determined from the fits significant to a 0.05 level. The expected error of the prediction is below 25 % at any percentile. These errors are half to one third of the errors expected with the best prediction models available to date.

AB - A model for fragmentation in bench blasting that originates from dimensional analysis of fragmentation in asteroid collisions is presented. Percentiles of the size distribution are obtained in the basic model as the product of a rock strength-to-explosive energy ratio, a bench shape factor, a scale factor or characteristic size and a percentage passing-related factor. The parameters of the model are fitted to 169 bench blasts in different sites and rock types, bench geometries and delay times, for which the fragmentation of the muckpile was obtained by sieving. The basic model is found to significantly improve with an additional factor describing the rock mass structure in terms of the spacing andorientation of discontinuities, and another one describing the delay between successive contiguous shots; the latter is conveniently formulated as a function of the P-wave velocity and the holes spacing. The rock strength property chosen is the strain energy at rupture that, together with the explosive energy density in the rock (or energy powder factor), forms a combined rock strength/explosive energy nondimensionalfactor. The model, called xP-frag, is applicable from 5 to 100 percentile fragment sizes, with all parameters determined from the fits significant to a 0.05 level. The expected error of the prediction is below 25 % at any percentile. These errors are half to one third of the errors expected with the best prediction models available to date.

M3 - Conference contribution

VL - 43

SP - 265

EP - 280

BT - Proc 43rd ISEE Conference on Explosives and Blasting Technique

CY - Cleveland, OH

ER -