Application of various robust techniques to study and evaluate the role of effective parameters on rock fragmentation

Mehrdanesh, Amirhossein; Monjezi, Masoud; Khandelwal, Manoj; Bayat, Parichehr

doi:10.1007/s00366-021-01522-4

Cited by 4 publications

(1 citation statement)

References 65 publications

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“…According to Ghorbani et al [34], rock hardness affects the rock mechanics and excavatability. Mehrdanesh et al [35] also noted that hard rocks require more energy for fragmentation, and the understanding of hardness is essential for selecting appropriate explosive strategies.…”

Section: Rock Properties Resultsmentioning

confidence: 99%

Evaluation of Rock and Explosive Properties for Fragmentation Characterization: An Application of WipFrag

Otokiti,

Adebayo

2024

IJEATS

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This study investigates the rock explosive properties in selected Lokoja quarries, Nigeria, with the goal of characterizing fragmentation for optimized downstream operations. The analysis includes porosity, UCS values, and permeability assessment in Gitto quarry, highlighting advantages in material application. Comparisons between rock formations (Q1 and Q2) reveal varying compressive strengths, crucial for determining appropriate explosive energy for efficient fragmentation. Blast design parameters from Q1 and Q2 indicate consistent values, aiding in operational planning. Fragmentation analysis, conducted using WipFrag software, delineates size ranges and classifies the blast as having a moderate distribution. Correlations between blast fragmentation size and powder factor underscore the impact on efficiency. A classification chart and table are presented for convenient interpretation of results, providing valuable insights for enhancing blasting practices in Lokoja quarries and ultimately improving productivity. The fragmentation analysis result carried out in this study using WipFrag software shows that the 50%, 80% and Maximum block size passing size ranges from 539.94 – 1349.53 mm, 690.07 – 1907.81 mm, 808-2280 mm respectively. The blast fragmentation sizes are classified as moderate distribution blast based on the uniformity index value ranging from 1.95 to 2.4. The relationship between blast fragmentation size and powder factor was evaluated using linear correlation coefficient. It was noted that, X20, X50, X80 has high R2 values greater than 60% respectively

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Section: Rock Properties Resultsmentioning

confidence: 99%

Evaluation of Rock and Explosive Properties for Fragmentation Characterization: An Application of WipFrag

Otokiti,

Adebayo

2024

IJEATS

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Predicting tunnel squeezing using support vector machine optimized by whale optimization algorithm

et al. 2022

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Review of machine learning application in mine blasting

2023

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Mine blasting has adopted machine learning (ML) into its practices with the aims of performance optimization, better decision-making process, and work safety. This study is aimed at reviewing the status of ML method applications to mine blasting issues. One of the most important observations of this research highlights the developed ML methods such as hybrids/ensembles, outperforming the other methods at 61% of the sample of case studies. The first section provides a background on the application of ML methods in mining. Two sections of the review provide the trends in the application of ML methods and the utilization of input parameters in surface and underground blasting problems. The appraisal reveals an increase of hybrid/ensemble or highly developed ML methods for the top four blast issues on the surface (72%) and underground (45%). The sample of studies reviewed indicated through graphical/statistical means a continuing increase in hybrids/ensembles’ use mirrored by high research output for the top four surface blast issues. This is contrasted by a low rate of research in underground blasting, under the encountered operational conditions applied. Regarding the input parameters, controllable parameters (blast design and geometry) were recognized to be steadily used in surface blasting for the top four surface blast issues, along with less involvement from the uncontrollable parameters (geological and geotechnical parameters). On the contrary, underground blasting has a slight involvement of the uncontrollable parameters more than the controllable parameters, in the top four blast issues. In the final section of this paper, the review offers a discussion of the current state of research under the encountered limits and where the efforts should be focused concerning ML methods applied, input parameters involved, and the challenges faced. Such high levels of performances are in demand in a highly complex mining environment. Persistent research, and development of mining employees’ technological skills alongside an increased awareness among mining industry of the benefits of highly developed ML techniques, is greatly needed at this stage. This would establish the role of highly developed ML methods in improving both the blasting process and the overall decision-making and mining management.

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Application of various robust techniques to study and evaluate the role of effective parameters on rock fragmentation

Cited by 4 publications

References 65 publications

Evaluation of Rock and Explosive Properties for Fragmentation Characterization: An Application of WipFrag

Evaluation of Rock and Explosive Properties for Fragmentation Characterization: An Application of WipFrag

Predicting tunnel squeezing using support vector machine optimized by whale optimization algorithm

Review of machine learning application in mine blasting

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