• Abinash Parida

Surface mining, a predominant method in the mining industry, frequently employs drilling and blasting for rock excavation. While this method efficiently fragments and displaces rock, it also induces ground vibrations, an environmental concern that can impact structures. These vibrations, primarily described by Peak Particle Velocity (PPV), result from shock waves behaving visco-elastically in the elastic zone. Traditional empirical models, based on parameters like maximum charge per delay and distance from the blast, often inaccurately predict PPV and fail to estimate dominant frequencies. To address these limitations, Artificial Neural Networks (ANN) have emerged as a superior predictive tool. By incorporating multiple parameters such as explosive charge, distance, stemming depth, and rock properties, ANNs provide more accurate PPV and frequency predictions. Studies by Khandelwal and Singh (2006, 2007), Mohammad (2009), Dehghani and Ataee-pour (2011), Alvarez-Vigil et al. (2012), and Mahdi Saadat et al. (2014) have demonstrated the effectiveness of ANN models in various mining contexts. Data from geophones installed in Orissa iron mines further validated the versatility of neural networks in predicting blast-induced ground vibrations. This abstract underscores the significant advancements in vibration prediction through ANN, offering enhanced precision over traditional empirical methods.

Key words: Surface mining, Rock excavation, Drilling and blasting, Ground vibration, Peak Particle Velocity (PPV), Empirical models, Artificial Neural Networks (ANN), Blast-induced vibrations, Vibration prediction, Geophones, Elastic properties, Explosive charge, Stemming depth, Rock properties, Frequency prediction

"Advancements in Predicting Blast-Induced Ground Vibrations in Surface Mining Using Artificial Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.b268-b282, December-2024, Available :http://www.jetir.org/papers/JETIR2412122.pdf

"Advancements in Predicting Blast-Induced Ground Vibrations in Surface Mining Using Artificial Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. ppb268-b282, December-2024, Available at : http://www.jetir.org/papers/JETIR2412122.pdf