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Open Access Research Article

Coupling Bayesian Neural Network and Linear Regression Approach for High-Strength Rock Fragmentation Energy Optimization

by Blessing Olamide Taiwo 1 ,  Yewuhalashet Fissha 2, 3,* ,  Esma Kahraman 4 ,  Hawraa H. Abbas 5,6 ,  Victor Afolabi Jebutu 7 ,  Adams Abiodun Akinlabi 1 ,  Shahab Hosseini 8  and  Ahsan Rabbani 9,*
1
Department of Mining Engineering, Federal University of Technology, Akure Nigeria
2
Department of Geosciences, Geotechnology and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, Japan
3
Department of Mining Engineering, Aksum University, 7080, Aksum, Tigray, Ethiopia
4
Department of Mining Engineering, Cukurova University, Adana 01250, Turkey
5
College of Information Technology Engineering, Al-Zahraa University for Women 56001 Karbala, Iraq
6
Department of Electrical and Electronics Engineering, University of Kerbala, Karbala 56001, Iraq
7
Department of Engineering Management, University of Bolton, England
8
Faculty of Engineering, Tarbiat Modares University, Tehran
9
Department of Civil Engineering, Sai Nath University, Ranchi (Jharkhand), India
*
Author to whom correspondence should be addressed.
RML  2025 2(2):14; https://doi.org/10.70425/rml.202502.14
Received: 28 September 2024 / Accepted: 5 May 2025 / Published Online: 17 June 2025
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Abstract

The utilization of blasting has been prevalent in mining and civil engineering domains due to its cost-effectiveness and affordability as a method for fracturing rock. The achievement of an ideal blast results in the most effective fragmentation while ensuring safety, cost-effectiveness, and environmental sustainability. In developing blast efficiency model, this study considered uniaxial compressive strength (UCS), spacing, hole depth, stemming length, point load index, powder factor, charge weight and burden. The model was trained by artificial neural network (ANN) and linear multivariate regression (LMVR) using 85 production datasets. After training four-layer ANN architecture 8-4-1 was found to be optimum. The prediction accurateness of two developed models was analysed using mean square error (MSE), performance index (PI), variance accounted for (VAF), root mean square error (RMSE), and co-efficient of determination (R2). The obtained values of performance parameters reveals ANN model to be more accurate as compared to the LMVR model. The ANN and LMVR model have R2 value of 90.9% and 56.3%. The optimized model was employed to achieve the optimal blast design for high strength granite to enhance blast efficiency. The test data result was utilized to optimize the blast parameters, including spacing, stemming length, burden, charge length, and charge. The values chosen for these parameters were 1.8 m, 1.8 m, 1.5 m, 157.17 kg, and 1.35 kg/m3, respectively, based on the optimum model.

Keywords: blasting efficiency; granite; soft computing model; blast parameter classification; WipFrag Software;
Copyright: © 2025 by Taiwo, Fissha, Kahraman, Abbas, Jebutu, Akinlabi, Hosseini and Rabbani. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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ACS Style
Taiwo, B. O.; Fissha, Y.; Kahraman, E.; Abbas, H. H.; Jebutu, V. A.; Akinlabi, A. A.; Hosseini, S.; Rabbani, A. Coupling Bayesian Neural Network and Linear Regression Approach for High-Strength Rock Fragmentation Energy Optimization. Rock Mechanics Letters, 2025, 2, 14. doi:10.70425/rml.202502.14
AMA Style
Taiwo B O, Fissha Y, Kahraman E et al.. Coupling Bayesian Neural Network and Linear Regression Approach for High-Strength Rock Fragmentation Energy Optimization. Rock Mechanics Letters; 2025, 2(2):14. doi:10.70425/rml.202502.14
Chicago/Turabian Style
Taiwo, Blessing O.; Fissha, Yewuhalashet; Kahraman, Esma; Abbas, Hawraa H.; Jebutu, Victor A.; Akinlabi, Adams A.; Hosseini, Shahab, and et al. 2025. "Coupling Bayesian Neural Network and Linear Regression Approach for High-Strength Rock Fragmentation Energy Optimization" Rock Mechanics Letters 2, no.2:14. doi:10.70425/rml.202502.14
APA Style
Taiwo, B. O., Fissha, Y., Kahraman, E., Abbas, H. H., Jebutu, V. A., Akinlabi, A. A., Hosseini, S., & Rabbani, A. (2025). Coupling Bayesian Neural Network and Linear Regression Approach for High-Strength Rock Fragmentation Energy Optimization. Rock Mechanics Letters, 2(2), 14. doi:10.70425/rml.202502.14

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