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Artificial Neural Network Modeling as an Approach to Limestone Blast Production Rate Prediction: a Comparison of PI-BANN and MVR Models

Publicated to:Journal Of Mining And Environment. 14 (2): 375-388 - 2023-01-01 14(2), DOI: 10.22044/jme.2023.12489.2266

Authors: Taiwo, BO; Gebretsadik, A; Fissha, Y; Kide, Y; Li, EM; Haile, K; Oni, OA

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Abstract

Rock blast production rate (BPR) is one of the most crucial factors in the evaluation of mine project's performance. In order to improve the production of a limestone mine, the blast design parameters and image analysis results are used in this work to evaluate the BPR. Additionally, the effect of rock strength on BPR is determined using the blast result collected. In order to model BPR prediction using artificial neural networks (ANNs) and multivariate prediction techniques, a total of 219 datasets with 8 blasting influential parameters from limestone mine blasting in India are collected. To obtain a high-accuracy model, a new training process called the permutation important-based Bayesian (PI-BANN) training approach is proposed in this work. The developed models are validated with new 20 blast rounds, and evaluated with two model performance indices. The validation result shows that the two model results agree well with the BPR practical records. Additionally, compared to the MVR model, the proposed PI-BANN model in this work provides a more accurate result. Based on the controllable parameters, the two models can be used to predict BPR in a variety of rock excavation techniques. The study result reveals that rock strength variation affects both the blast outcome (BPR) and the quantity of explosives used in each blast round.

Keywords

algorithmannartificial neural networkblasting improvementmachine learningmodel prediction evaluationoptimizationpermutation important-basedregression-analysisrock fragmentationsarcheshmeh copper minesimulationArtificial neural networkBlasting improvementMachine learningMean particle-sizeModel prediction evaluationPermutation important-basedRock fragmentation

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Journal Of Mining And Environment, and although the journal is classified in the quartile Q3 (Agencia WoS (JCR)), its regional focus and specialization in Mining & Mineral Processing, give it significant recognition in a specific niche of scientific knowledge at an international level.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.49. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 2.13 (source consulted: FECYT Feb 2024)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-06-03, the following number of citations:

  • WoS: 6
  • Scopus: 10

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-03:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 20 (PlumX).

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: China; Ethiopia; Japan; Niger; Nigeria.