- Tytuł:
- 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 - Powiązania:
- https://bibliotekanauki.pl/articles/219872.pdf
- Data publikacji:
- 2020
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
strzałowy
wibracje podłoża
kopalnia miedzi Sarcheshmeh
blasting
ground vibration
gene expression programming
linear multivariate regression
Sarcheshmeh copper mine - Opis:
- 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.
- Źródło:
-
Archives of Mining Sciences; 2020, 65, 2; 317-335
0860-7001 - Pojawia się w:
- Archives of Mining Sciences
- Dostawca treści:
- Biblioteka Nauki
Artykuł