Evaluation and prediction of blast-induced peak particle velocity using artificial neural network: A case study. (March 2018)

• Record Type: Journal Article
• Authors: Ragam, Prashanth; Nimaje, Devidas Sahebraoji
• Is Part Of: Noise & vibration worldwide. Volume 49:Number 3(2018)
• Page Start: 111
• Page End: 119
• Publication Date: 2018-03
• Subjects: Peak particle velocity -- blast-induced ground vibration -- artificial neural network -- conventional predictors -- multiple linear regression -- coefficient of determination -- root mean square error; Noise control -- Periodicals; Damping (Mechanics) -- Periodicals; Soundproofing -- Periodicals; Damping (Mechanics); Noise control; Soundproofing; Periodicals; 620.205
• DOI: 10.1177/0957456518763161
• Languages: English
• ISSNs: 0957-4565

Abstract

Over the past few decades, inducing of ground vibrations from blasting may cause severe damage to surrounding structures, plants, and human beings in the mining industry. Therefore, it is essential to monitor and predict the ambiguous vibration levels and take measures to reduce their hazardous effect. In this study, to evaluate and predict the ambiguous ground vibrations, an application of artificial neural network technique was used. A three-layer, feed-forward back-propagation multilayer perception neural network having six input parameters, the distance from blast face, maximum charge per delay, spacing, burden, hole depth and a number of holes, and one output: peak particle velocity, was used and trained with the Levenberg–Marquardt algorithm using 25 experimental and blast event records from the iron ore mine A, India. To determine the efficiency and accuracy of the developed artificial neural network model, seven conventional predictor models proposed by the US Bureau of Mines, Ambraseys–Hendron, Langefors–Kihlstrom, general predictor, Ghosh–Daemen predictor, cardiac magnetic resonance imaging (CMRI) predictor, Bureau of Indian Standards, as well as multiple linear regression, were applied to establish a relation between peak particle velocity and its influencing parameters. The obtained results reveal that the proposed artificial neural network model can estimate ground vibrations more accurately as compared with the various conventional predictor models available. Coefficient of determination (R 2 ) and root mean square error indices were obtained as 0.9971 and 0.08133 for artificial neural network model, respectively.