Abstract
Blasting performance is influenced by mechanical and structural properties of the rock, on one side, and blast design parameters
on the other. This paper describes a new methodology to assess rock mass quality from drill-monitoring data to guide
blasting in open pit operations. Principal component analysis has been used to combine measurement while drilling (MWD)
information from two drill rigs; corrections of the MWD parameters to minimize external influences other than the rock
mass have been applied. First, a Structural factor has been developed to classify the rock condition in three classes (massive,
fractured and heavily fractured). From it, a structural block model has been developed to simplify the recognition of
rock classes. Video recording of the inner wall of 256 blastholes has been used to calibrate the results obtained. Secondly, a
combined strength-grade factor has been obtained based on the analysis of the rock type description and strength properties
from geology reports, assaying of drilling chips (ore/waste identification) and 3D unmanned aerial vehicle reconstructions
of the post-blast bench face. Data from 302 blastholes, comprised of 26 blasts, have been used for this analysis. From the
results, four categories have been identified: soft-waste, hard-waste, transition zone and hard-ore. The model determines
zones of soft and hard waste rock (schisted sandstone and limestone, respectively), and hard ore zones (siderite rock type).
Finally, the structural block model has been combined with the strength-grade factor in an overall rock factor. This factor,
exclusively obtained from drill monitoring data, can provide an automatic assessment of rock structure, strength, and waste/
ore identification.
on the other. This paper describes a new methodology to assess rock mass quality from drill-monitoring data to guide
blasting in open pit operations. Principal component analysis has been used to combine measurement while drilling (MWD)
information from two drill rigs; corrections of the MWD parameters to minimize external influences other than the rock
mass have been applied. First, a Structural factor has been developed to classify the rock condition in three classes (massive,
fractured and heavily fractured). From it, a structural block model has been developed to simplify the recognition of
rock classes. Video recording of the inner wall of 256 blastholes has been used to calibrate the results obtained. Secondly, a
combined strength-grade factor has been obtained based on the analysis of the rock type description and strength properties
from geology reports, assaying of drilling chips (ore/waste identification) and 3D unmanned aerial vehicle reconstructions
of the post-blast bench face. Data from 302 blastholes, comprised of 26 blasts, have been used for this analysis. From the
results, four categories have been identified: soft-waste, hard-waste, transition zone and hard-ore. The model determines
zones of soft and hard waste rock (schisted sandstone and limestone, respectively), and hard ore zones (siderite rock type).
Finally, the structural block model has been combined with the strength-grade factor in an overall rock factor. This factor,
exclusively obtained from drill monitoring data, can provide an automatic assessment of rock structure, strength, and waste/
ore identification.
Originalsprache | Englisch |
---|---|
Seiten (von - bis) | 3209-3228 |
Seitenumfang | 20 |
Fachzeitschrift | Rock mechanics and rock engineering |
Jahrgang | 54.2021 |
Ausgabenummer | June |
DOIs | |
Publikationsstatus | Veröffentlicht - 17 Apr. 2021 |
Bibliographische Notiz
Funding Information:This work has been conducted within project “SLIM” funded by the European Union’s Horizon 2020 research and innovation program under grant agreement no. 730294. The authors would like to acknowledge VA Erzberg GmbH for their valuable input into the project.
Funding Information:
This work has been conducted within project ?SLIM? funded by the European Union?s Horizon 2020 research and innovation program under grant agreement no. 730294. The authors would like to acknowledge VA Erzberg GmbH for their valuable input into the project.
Publisher Copyright:
© 2021, The Author(s).