Blessing Taiwo - Department of Mining Engineering, Federal University of Technology, Akure, Nigeria
Gebretsadik Angesom - Department of Mining Engineering, Aksum University, Aksum, Tigray, Ethiopia
Yewuhalashet Fissha - Department of Mining Engineering, Aksum University, Aksum, Tigray, Ethiopia
Yemane Kide - Department of Mining Engineering, Aksum University, Aksum, Tigray, Ethiopia
Enming Li - School of Resources and Safety Engineering, Central South University, Changsha, China
Kiross Haile - Ethiopian Ministry of Mines, Mineral Industry Development Institute, Addis Ababa, Ethiopia
Oluwaseun Oni - Department of Mining Engineering, Federal University of Technology, Akure, Nigeria

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 ۲۱۹ datasets with ۸ 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 ۲۰ 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.

rock fragmentation, blasting improvement, soft computing models, model prediction evaluation, Machine learning