Developing an Advanced Soft Computational Model for Estimating Blast-Induced Ground Vibration in Nui Beo Open-pit Coal Mine (Vietnam) Using Artificial Neural Network

Nguyen, Hoang; Bui, Xuan‑Nam; Tran, Quang Hieu; Nguyen, Quoc Long; Vu, Dinh Hieu; Pham, Van Hoa; Le, Qui Thao; Nguyen, Phu Vu

doi:10.29227/IM-2019-02-58

Artykuł - szczegóły

Tytuł artykułu

Developing an Advanced Soft Computational Model for Estimating Blast-Induced Ground Vibration in Nui Beo Open-pit Coal Mine (Vietnam) Using Artificial Neural Network

Autorzy

Nguyen Hoang , Bui Xuan‑Nam , Tran Quang Hieu , Nguyen Quoc Long , Vu Dinh Hieu , Pham Van Hoa , Le Qui Thao , Nguyen Phu Vu

Treść / Zawartość

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Identyfikatory

DOI

10.29227/IM-2019-02-58

Warianty tytułu

Opracowanie zaawansowanego modelu obliczeniowego do szacowania wibracji gruntu wywołanych wybuchem w odkrywkowej kopalni węgla Nui Beo (Wietnam) przy użyciu sztucznej sieci neuronowej

Konferencja

POL-VIET 2019 : scientific-research cooperation between Poland and Vietnam : 08–10.07.2019, Krakow

Języki publikacji

Abstrakty

The principal object of this study is blast-induced ground vibration (PPV), which is one of the dangerous side effects of blasting operations in an open-pit mine. In this study, nine artificial neural networks (ANN) models were developed to predict blast-induced PPV in Nui Beo open-pit coal mine, Vietnam. Multiple linear regression and the United States Bureau of Mines (USBM) empirical techniques are also conducted to compare with nine developed ANN models. 136 blasting operations were recorded in many years used for this study with 85% of the whole datasets (116 blasting events) was used for training and the rest 15% of the datasets (20 blasting events) for testing. Root Mean Square Error (RMSE), Determination Coefficient (R2), and Mean Absolute Error (MAE) are used to compare and evaluate the performance of the models. The results revealed that ANN technique is more superior to other techniques for estimating blast-induced PPV. Of the nine developed ANN models, the ANN 7-10-8-5-1 model with three hidden layers (ten neurons in the first hidden layer, eight neurons in the second layers, and five neurons in the third hidden layer) provides the most outstanding performance with an RMSE of 1.061, R2 of 0.980, and MAE of 0.717 on testing datasets. Based on the obtained results, ANN technique should be applied in preliminary engineering for estimating blast-induced PPV in open-pit mine.

Słowa kluczowe

Vietnam open pit mining artificial neural network

Wietnam górnictwo odkrywkowe sieci neuronowe

Wydawca

Polskie Towarzystwo Przeróbki Kopalin

Czasopismo

Inżynieria Mineralna

Rocznik

2019

Tom

R. 21, nr 2/2

Strony

58--73

Opis fizyczny

Bibliogr. 64 poz., tab., wykr., zdj.

Twórcy

autor

Nguyen Hoang

Hanoi University of Mining and Geology, Hanoi, Vietnam
Center for Mining, Electro-Mechanical research, Hanoi, Vietnam

autor

Bui Xuan‑Nam

Hanoi University of Mining and Geology, Hanoi, Vietnam
Center for Mining, Electro-Mechanical research, Hanoi, Vietnam

autor

Tran Quang Hieu

Hanoi University of Mining and Geology, Hanoi, Vietnam
Center for Mining, Electro-Mechanical research, Hanoi, Vietnam

autor

Nguyen Quoc Long

Hanoi University of Mining and Geology, Hanoi, Vietnam
Center for Mining, Electro-Mechanical research, Hanoi, Vietnam

autor

Vu Dinh Hieu

Hanoi University of Mining and Geology, Hanoi, Vietnam
Center for Mining, Electro-Mechanical research, Hanoi, Vietnam

autor

Pham Van Hoa

Hanoi University of Mining and Geology, Hanoi, Vietnam
Center for Mining, Electro-Mechanical research, Hanoi, Vietnam

autor

Le Qui Thao

Hanoi University of Mining and Geology, Hanoi, Vietnam
Center for Mining, Electro-Mechanical research, Hanoi, Vietnam

autor

Nguyen Phu Vu

Hanoi University of Mining and Geology, Hanoi, Vietnam
Mining - Geology Design Survey and Construction Consulting JSC, Hanoi, Vietnam

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-47db08af-f534-49d7-a08a-7e2ffdba41a1