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Estimation of air overpressure using bat algorithm

Dehghani Hesam, Ali Mohammad Nia Farid

Mining Science

2021

Vol. 28

77-92

Air overpressure (AOp) is an undesirable phenomenon in blasting operations. Due to high potential to cause damage to nearby structures and to cause injuries, to personnel or animals, AOp is one of the most dangerous adverse effect of blasting. For controlling and decreasing the effect of this phenomenon, it is necessary to predict it. Because of multiplicity of effective parameters and complexity of interactions among these parameters, empirical methods may not be fully appropriate for AOp estimation. The scope of this study is to predict AOp induced by blasting through a novel approach based on the bat algorithm. For this purpose, the parameters of 62 blasting operations were accurately recorded and AOp were measured for each operation. In the next stage, a new empirical predictor was developed to predict AOp. The results clearly showed the superiority of the proposed bat algorithm model in comparison with the empirical approaches.

Prediction of blast-induced ground vibration using gene expression programming (GEP), artificial neural networks (ANNS), and linear multivariate regression (LMR)

Shakeri Jamshid, Shokri Behshad Jodeiri, Dehghani Hesam

Archives of Mining Sciences

2020

Vol. 65, no. 2

317--335

In this paper, an attempt was made to find out two empirical relationships incorporating linear mul-tivariate regression (LMR) and gene expression programming (GEP) for predicting the blast-induced ground vibration (BIGV) at the Sarcheshmeh copper mine in south of Iran. For this purpose, five types of effective parameters in the blasting operation including the distance from the blasting block, the burden, the spacing, the specific charge, and the charge per delay were considered as the input data while the output parameter was the BIGV. The correlation coefficient and root mean squared error for the LMR were 0.70 and 3.18 respectively, while the values for the GEP were 0.91 and 2.67 respectively. Also, for evaluating the validation of these two methods, a feed-forward artificial neural network (ANN) with a 5-20-1 structure has been used for predicting the BIGV. Comparisons of these parameters revealed that both methods successfully suggested two empirical relationships for predicting the BIGV in the case study. However, the GEP was found to be more reliable and more reasonable.