Prediction of Backbreak in the Blasting Operations using Artificial Neural Network (ANN) Model and Statistical Models (Case study: Gol-e-Gohar Iron Ore Mine No. 1)
Author:
Publisher
Polish Academy of Sciences Chancellery
Subject
Geochemistry and Petrology,Geotechnical Engineering and Engineering Geology
Link
https://journals.pan.pl/Content/122702/PDF/Archiwum-67-1-07-Abbas.pdf
Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Applications of Soft Computing Methods in Backbreak Assessment in Surface Mines: A Comprehensive Review;Computer Modeling in Engineering & Sciences;2024
2. Refined Design and Optimization of Underground Medium and Long Hole Blasting Parameters—A Case Study of the Gaofeng Mine;Mathematics;2023-03-27
3. Artificial Neural Network Modeling as an Approach to Limestone Blast Production Rate Prediction: a Comparison of PI-BANN and MVR Models;J MIN ENVIRON;2023
4. A Hybrid Model for Back-Break Prediction using XGBoost Machine learning and Metaheuristic Algorithms in Chadormalu Iron Mine;J MIN ENVIRON;2023
5. Prediction of blast-induced air overpressure using a hybrid machine learning model and gene expression programming (GEP): A case study from an iron ore mine;AIMS Geosciences;2023
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