- Tytuł:
- Soft computing-based technique as a predictive tool to estimate blast-induced ground vibration
- Autorzy:
-
Arthur, Clement Kweku
Temeng, Victor Amoako
Ziggah, Yao Yevenyo - Powiązania:
- https://bibliotekanauki.pl/articles/1839011.pdf
- Data publikacji:
- 2019
- Wydawca:
- Główny Instytut Górnictwa
- Tematy:
-
radial basis function neural network
back propagation neural network
generalized regression neural network
wavelet neural network
group method of data handling
ground vibration
radialna funkcja bazowa
sieć neuronowa
GRNN
sieć falkowo-neuronowa
grupowa metoda przetwarzania danych
drgania gruntu - Opis:
- The safety of workers, the environment and the communities surrounding a mine are primary concerns for the mining industry. Therefore, implementing a blast-induced ground vibration monitoring system to monitor the vibrations emitted due to blasting operations is a logical approach that addresses these concerns. Empirical and soft computing models have been proposed to estimate blast-induced ground vibrations. This paper tests the efficiency of the Wavelet Neural Network (WNN). The motive is to ascertain whether the WNN can be used as an alternative to other widely used techniques. For the purpose of comparison, four empirical techniques (the Indian Standard, the United State Bureau of Mines, Ambrasey-Hendron, and Langefors and Kilhstrom) and four standard artificial neural networks of backpropagation (BPNN), radial basis (RBFNN), generalised regression (GRNN) and the group method of data handling (GMDH) were employed. According to the results obtained from the testing dataset, the WNN with a single hidden layer and three wavelons produced highly satisfactory and comparable results to the benchmark methods of BPNN and RBFNN. This was revealed in the statistical results where the tested WNN had minor deviations of approximately 0.0024 mm/s, 0.0035 mm/s, 0.0043 mm/s, 0.0099 and 0.0168 from the best performing model of BPNN when statistical indicators of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Root Mean Square Error (RRMSE), Correlation Coefficient (R) and Coefficient of determination (R2) were considered.
- Źródło:
-
Journal of Sustainable Mining; 2019, 18, 4; 287-296
2300-1364
2300-3960 - Pojawia się w:
- Journal of Sustainable Mining
- Dostawca treści:
- Biblioteka Nauki
Artykuł