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Study of rock fracture under blast loading

Baranowski Paweł, Kucewicz Michał, Pytlik Mateusz, Małachowski Jerzy

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2022

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Vol. 70, nr 5

art. no. e141723

EN

A study of dolomite rock material failure using a simple small-scale blast setup is presented. Laboratory tests were conducted using disc specimens drilled with a borehole in the center. A detonation cord and a blasting cap were fitted inside the borehole to induce cracking and fracturing of the specimens. The specimens were inserted between two steel plates, which were compressed against the specimen using bolt screws. Prior to testing, the most suitable screw torque for constraining the vertical displacement of the specimen surfaces without compressing the specimen was selected based on numerical simulations. Then, the experimental tests with the blasting cap were simulated using the Johnson–Holmquist II (JH-2) material model, and the properties of the blasting cap were determined and verified in two special tests with a lead specimen. Possessing the validated model, the influence of specimen thickness on the cracking patterns was finally analyzed. This paper presents a relatively easy method for studying rock material behavior under blast loading and for validating the numerical and constitutive models used for rock simulations.

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Wpływ budowy gelogicznej skał na projekt robót strzałowych. Cz. I

Grześkowiak Arkadiusz

Kruszywa : produkcja - transport - zastosowanie

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2021

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Nr 2

52--57

PL

W artykule przedstawiono główne problemy związane identyfikacją właściwości skał przeznaczonych do urabiania za pomocą materiałów wybuchowych.

EN

The article presents main problems related to identifying the properties of rocks intended for mining with explosives. Methods of determining rock properties and their application in blast design are indicated, as well as possible effects resulting from their incorrect evaluation. Methods of verifying initial geological structures parameters based on drilling works and possibilities of using available information to improve blasting effects are also described.

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Prediction of blast-induced ground vibration using gene expression programming (GEP), artificial neural networks (ANNS), and linear multivariate regression (LMR)

Shakeri Jamshid, Shokri Behshad Jodeiri, Dehghani Hesam

Archives of Mining Sciences

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2020

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Vol. 65, no. 2

317--335

EN

In this paper, an attempt was made to find out two empirical relationships incorporating linear mul-tivariate regression (LMR) and gene expression programming (GEP) for predicting the blast-induced ground vibration (BIGV) at the Sarcheshmeh copper mine in south of Iran. For this purpose, five types of effective parameters in the blasting operation including the distance from the blasting block, the burden, the spacing, the specific charge, and the charge per delay were considered as the input data while the output parameter was the BIGV. The correlation coefficient and root mean squared error for the LMR were 0.70 and 3.18 respectively, while the values for the GEP were 0.91 and 2.67 respectively. Also, for evaluating the validation of these two methods, a feed-forward artificial neural network (ANN) with a 5-20-1 structure has been used for predicting the BIGV. Comparisons of these parameters revealed that both methods successfully suggested two empirical relationships for predicting the BIGV in the case study. However, the GEP was found to be more reliable and more reasonable.