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

• Autorzy: Shakeri Jamshid , Shokri Behshad Jodeiri , Dehghani Hesam

Identyfikatory

DOI

10.24425/ams.2020.133195

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

blasting; ground vibration; gene expression programming; linear multivariate regression; Sarcheshmeh copper mine

PL

strzałowy; wibracje podłoża; kopalnia miedzi Sarcheshmeh

• Wydawca: Instytut Mechaniki Górotworu PAN
• Czasopismo: Archives of Mining Sciences
• Rocznik: 2020
• Tom: Vol. 65, no. 2
• Strony: 317--335
• Opis fizyczny: Bibliogr. 21 poz., rys., tab., wykr.

Twórcy

autor

Shakeri Jamshid

• Department of Mining Engineering, Hamedan University of Technology, Hamedan, Iran

autor

Shokri Behshad Jodeiri

• Department of Mining Engineering, Hamedan University of Technology, Hamedan, Iran

autor

Dehghani Hesam

• Department of Mining Engineering, Hamedan University of Technology, Hamedan, Iran

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020)