Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network

Journal of Rock Mechanics and Geotechnical Engineering

Danial Jahed Armaghani¹  Hoang Nguyen²  XuanNam Bui³  Bhatawdekar Ramesh Murlidhar⁴  Prashanth Ragam⁵  Jamal Rostami⁶  Edy Tonnizam Mohamad⁶ 

[1] Corresponding author. Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, Hanoi, 100000, Viet Nam.;Department of Mining Engineering, Indian Institute of Technology, Kharagpur, 721302, India;Innovations for Sustainable and Responsible Mining (ISRM) Group, Hanoi University of Mining and Geology, Hanoi, 100000, Viet Nam;Centre of Tropical Geoengineering (Geotropik), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Johor, 81310, Malaysia;Department of Mining Engineering, Earth Mechanics Institute, Colorado School of Mines, Golden, CO, 80401, USA;Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, Hanoi, 100000, Viet Nam;

关键词: Flyrock;    Harris hawks optimization (HHO);    Multi-layer perceptron (MLP);    Random forest (RF);    Support vector machine (SVM);    Whale optimization algorithm (WOA);   

DOI  :  

来源: DOAJ

【 摘 要 】

In mining or construction projects, for exploitation of hard rock with high strength properties, blasting is frequently applied to breaking or moving them using high explosive energy. However, use of explosives may lead to the flyrock phenomenon. Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans, especially workers in the working sites. Thus, prediction of flyrock is of high importance. In this investigation, examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out. One hundred and fifty-two blasting events in three open-pit granite mines in Johor, Malaysia, were monitored to collect field data. The collected data include blasting parameters and rock mass properties. Site-specific weathering index (WI), geological strength index (GSI) and rock quality designation (RQD) are rock mass properties. Multi-layer perceptron (MLP), random forest (RF), support vector machine (SVM), and hybrid models including Harris Hawks optimization-based MLP (known as HHO-MLP) and whale optimization algorithm-based MLP (known as WOA-MLP) were developed. The performance of various models was assessed through various performance indices, including a10-index, coefficient of determination (R2), root mean squared error (RMSE), mean absolute percentage error (MAPE), variance accounted for (VAF), and root squared error (RSE). The a10-index values for MLP, RF, SVM, HHO-MLP and WOA-MLP are 0.953, 0.933, 0.937, 0.991 and 0.972, respectively. R2 of HHO-MLP is 0.998, which achieved the best performance among all five machine learning (ML) models.

【 授权许可】

Unknown