MACHINE LEARNING MODELS FOR PREDICTING ROCK FRAGMENTATION IN GRANITE QUARRY BLASTING

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Published ID: IJNRD2504582

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AKINLABI, Adams Abiodun, ADEBAYO, Babatunde, ALFRED, Victor Ayodele, BAMIDELE, Hafeez Afolabi, OLUWALADE, Temiloluwa Alexander

Blasting, granite, Fragmentation, Machine Learning, Predictive Modelling, Soft Computing

Blasting operations in quarrying and mining require precise control over fragmentation to optimize downstream processes. Traditional fragmentation analysis methods often fail to capture all the variables impacting fragmentation. This study presents a soft-computing approach integrating Support Vector Regression (SVR), Random Forest (RF), Artificial Neural Networks (ANN), Linear Regression and the traditional Kuz-Ram models to predict characteristic particle size at D63, N, and fragmentation size-uniformity Index, Xc. Rock properties such as Uniaxial Compressive Strength (UCS), Young’s Modulus, and Poisson’s Ratio were utilized alongside blast design parameters like spacing, burden, drillhole diameter and drillhole length. Python, with libraries such as Pandas, Scikit-learn, Keras, and Numpy were used for the modelling, Model performances were evaluated using RMSE, RAE, and R², with RF demonstrating superior predictive capability in predicting both fragmentation index and characteristic particle sizes with R2 score of 0.6 and 0.7, respectively. Whereas ANN performed the worst with R2 score of -1549 and 142 for characteristic particle size and uniformity index, respectively, due to small dataset. The results highlight the potential of AI-driven models in optimizing blasting efficiency.

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AKINLABI, Adams Abiodun, ADEBAYO, Babatunde, ALFRED, Victor Ayodele, BAMIDELE, Hafeez Afolabi, & OLUWALADE, Temiloluwa Alexander (April-2025). MACHINE LEARNING MODELS FOR PREDICTING ROCK FRAGMENTATION IN GRANITE QUARRY BLASTING. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(4), f718-f725. https://ijnrd.org/papers/IJNRD2504582.pdf

Volume 10 Issue 4, April-2025

Pages : f718-f725

Paper Reg. ID: IJNRD_305938

Published Paper Id: IJNRD2504582

Research Area: Science and Technology

Author Type: Foreign Author

Country: Akure, Ondo State, Nigeria

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