Optimizing Flyrock Forecasting in Open-Pit Blasting Using Hybrid Machine Learning Models

Flyrock occurrences pose significant environmental and safety risks in open-pit blasting, threatening equipment, personnel, and potentially causing fatal accidents. This study intends to reduce the risk of flyrock by developing multiple machine learning approaches to estimate its throw range, utiliz...

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Vydané v:	Rock mechanics and rock engineering Ročník 58; číslo 11; s. 12523 - 12550
Hlavní autori:	Zhang, Yulin, Qiu, Yingui, Du, Kun, Nguyen, Hoang, Armaghani, Danial Jahed, Zhou, Jian
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Vienna Springer Vienna 01.11.2025 Springer Nature B.V
Predmet:	Algorithms Artificial intelligence Blasting Civil Engineering Datasets Dimensional analysis Earth and Environmental Science Earth Sciences Fuzzy logic Geophysics/Geodesy Learning algorithms Machine learning Mines Monte Carlo simulation Neural networks Optimization Original Paper Parameter identification Parameter sensitivity Quarries Real time Reliability Risk reduction Root-mean-square errors Sensitivity analysis Software reliability Statistical analysis Training Open-pit blasting optimization Puma optimizer algorithm Intelligent feedback system Flyrock distance prediction Light spectrum optimizer Random forest Interpretability analysis
ISSN:	0723-2632, 1434-453X
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