TY - GEN
T1 - Enhancing Blast Design Efficiency for Rock Fragmentation with Gradient Descent and Artificial Neural Networks
T2 - 4th International Conference on Computers and Artificial Intelligence Technology, CAIT 2023
AU - Saubi, Onalethata
AU - Gaopale, Kesalopa
AU - Jamisola, Rodrigo S.
AU - Suglo, Raymond S.
AU - Matsebe, Oduetse
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This paper presents a predictive model for blast-induced fragmentation at Jwaneng Diamond Mine in Botswana using a machine learning algorithm namely artificial neural networks (ANN). A dataset consisting of 70 blasts with seven blast design parameters was used. The ANN is optimized based on Monte Carlo method to explore the solution space that is modelled using the input parameters, and the overall fragmentation has been improved to at-least 80%. Root mean square error (RMSE) and determination coefficient (R2) indices were used to validate and compare the performance of the the different ANN models. ANN one model, with architecture 7-10-1 demonstrated superiority over the other ANN models in predicting fragmentation with the highest R2 value of 0.880 and lower RMSE of 0.481. The results of sensitivity analysis showed that spacing has the most influence on fragmentation while hole diameter has the least influence on fragmentation.
AB - This paper presents a predictive model for blast-induced fragmentation at Jwaneng Diamond Mine in Botswana using a machine learning algorithm namely artificial neural networks (ANN). A dataset consisting of 70 blasts with seven blast design parameters was used. The ANN is optimized based on Monte Carlo method to explore the solution space that is modelled using the input parameters, and the overall fragmentation has been improved to at-least 80%. Root mean square error (RMSE) and determination coefficient (R2) indices were used to validate and compare the performance of the the different ANN models. ANN one model, with architecture 7-10-1 demonstrated superiority over the other ANN models in predicting fragmentation with the highest R2 value of 0.880 and lower RMSE of 0.481. The results of sensitivity analysis showed that spacing has the most influence on fragmentation while hole diameter has the least influence on fragmentation.
UR - http://www.scopus.com/inward/record.url?scp=85190302948&partnerID=8YFLogxK
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U2 - 10.1109/CAIT59945.2023.10469523
DO - 10.1109/CAIT59945.2023.10469523
M3 - Conference contribution
AN - SCOPUS:85190302948
T3 - 2023 4th International Conference on Computers and Artificial Intelligence Technology, CAIT 2023
SP - 1
EP - 5
BT - 2023 4th International Conference on Computers and Artificial Intelligence Technology, CAIT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 13 December 2023 through 15 December 2023
ER -