A Simulation Approach to Predict Blasting-Induced Flyrock and Size of Thrown Rocks

 

Edy Tonnizam Mohamad

Associate Professor (Dr.), Universiti Teknologi Malaysia, Department of Geotechnics and Transportation,Faculty of Civil Engineering, 81310 UTM Skudai, Johor, Malaysia; e-mail: edy@utm.com

Danial Jahed Armaghani

PhD Student, Universiti Teknologi Malaysia, Department of Geotechnics and Transportation,Faculty of Civil Engineering, 81310 UTM Skudai, Johor, Malaysia; e-mail: danialarmaghani@yahoo.com

Mohsen Hajihassani

Researcher, Universiti Teknologi Malaysia, Department of Geotechnics and Transportation,Faculty of Civil Engineering, 81310 UTM Skudai, Johor, Malaysia; e-mail: mohsen_hajihassani@yahoo.com

Koohyar Faizi

Postgraduate Student, Universiti Teknologi Malaysia, Department of Geotechnics and Transportation,Faculty of Civil Engineering, 81310 UTM Skudai, Johor, Malaysia; e-mail: koohyar.faizi@yahoo.com

Aminaton Marto

Professor, Universiti Teknologi Malaysia, Department of Geotechnics and Transportation,Faculty of Civil Engineering, 81310 UTM Skudai, Johor, Malaysia; e-mail: aminaton@utm.my

 

 ABSTRACT

Bench blasting is the most common method of rock excavation in quarries and surface mines. Blasting has some environmental impact such as ground vibration, airblast, dust and fumes and flyrock. One of the undesirable phenomena in the blasting operation is flyrock, which is a propelled rock fragment by explosive energy beyond the blast area. Prediction of flyrock distance and size of the thrown rocks is a remarkable step in reduction and controlling the blasting accidents in blasting operations. Different empirical models have been developed to predict flyrock distance. However, due to complex relationships between blasting parameters and flyrock phenomena, empirical methods cannot take into account all of the relevant parameters. Artificial neural networks have revealed as valid approaches to analyze geotechnical problems and are mainly able to cover the limitation of the existing approaches. This paper presents an approach based on artificial neural network to predict flyrock distance and size of the thrown rocks in blasting operations. The obtained results demonstrate that artificial neural approach is applicable to predict flyrock distance and size of the thrown rocks in blasting operations.

Keywords: Blasting, Flyrock, Thrown rocks, Artificial Neural Networks.

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