ArticleHeliyon2024
Explosive utilization efficiency enhancement: An application of machine learning for powder factor prediction using critical rock characteristics.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Enhanced characterization of hydraulic conductivity via standard penetration test for sandy soils and weathered rocks.Scientific reports · 2025Article
- Evaluating the slope behavior for geophysical flow prediction with advanced machine learning combinations.Scientific reports · 2025Article
- Applications of machine learning in predicting rut depth in off-road environments.Scientific reports · 2025Article
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Authors and funding
8 authors.
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Abstract
Maximizing the use of explosives is crucial for optimizing blasting operations, significantly influencing productivity and cost-effectiveness in mining activities. This work explores the incorporation of machine learning methods to predict powder factor, a crucial measure for assessing the effectiveness of explosive deployment, using important rock characteristics. The goal is to enhance the accuracy of powder factor prediction by employing machine learning methods, namely decision tree models and artificial neural networks. The analysis finds key rock factors that have a substantial impact on the powder factor, hence enabling more accurate planning and execution of blasting operations. The analysis uses data from 180 blast rounds carried out at a dolomite mine in south-south Nigeria. It incorporates measures such as root mean square error (RSME), mean absolute error (MAE), R-squared (R
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