Prediction of Blast-Induced Ground Vibration in Open-Pit Mines Using a New Technique Based on Imperialist Competitive Algorithm and M5Rules

2019 ◽  
Vol 29 (2) ◽  
pp. 791-806 ◽  
Author(s):  
Qiancheng Fang ◽  
Hoang Nguyen ◽  
Xuan-Nam Bui ◽  
Trung Nguyen-Thoi
Keyword(s):  
Ground Vibration ◽  
Imperialist Competitive Algorithm ◽  
New Technique ◽  
Open Pit ◽  
Open Pit Mines ◽  
Competitive Algorithm ◽  
A New Technique

Predicting the ground vibration induced by mine blasting using imperialist competitive algorithm

2018 ◽  
Vol 35 (4) ◽  
pp. 1774-1787 ◽  
Author(s):  
Katayoun Behzadafshar ◽  
Fahimeh Mohebbi ◽  
Mehran Soltani Tehrani ◽  
Mahdi Hasanipanah ◽  
Omid Tabrizi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ground Vibration ◽  
Imperialist Competitive Algorithm ◽  
Empirical Models ◽  
Coefficient Of Determination ◽  
Content Type ◽  
Competitive Algorithm ◽  
Spacing Ratio ◽  
Artificial Neural

PurposeThe purpose of this paper is to propose three imperialist competitive algorithm (ICA)-based models for predicting the blast-induced ground vibrations in Shur River dam region, Iran.Design/methodology/approachFor this aim, 76 data sets were used to establish the ICA-linear, ICA-power and ICA-quadratic models. For comparison aims, artificial neural network and empirical models were also developed. Burden to spacing ratio, distance between shot points and installed seismograph, stemming, powder factor and max charge per delay were used as the models’ input, and the peak particle velocity (PPV) parameter was used as the models’ output.FindingsAfter modeling, the various statistical evaluation criteria such as coefficient of determination (R2) were applied to choose the most precise model in predicting the PPV. The results indicate the ICA-based models proposed in the present study were more acceptable and reliable than the artificial neural network and empirical models. Moreover, ICA linear model with theR2 of 0.939 was the most precise model for predicting the PPV in the present study.Originality/valueIn the present paper, the authors have proposed three novel prediction methods based on ICA to predict the PPV. In the next step, we compared the performance of the proposed ICA-based models with the artificial neural network and empirical models. The results indicated that the ICA-based models proposed in the present paper were superior in terms of high accuracy and have the capacity to generalize.

Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on Particle Swarm Optimization and XGBoost

2019 ◽  
Vol 29 (2) ◽  
pp. 711-721 ◽  
Author(s):  
Xiliang Zhang ◽  
Hoang Nguyen ◽  
Xuan-Nam Bui ◽  
Quang-Hieu Tran ◽  
Dinh-An Nguyen ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Soft Computing ◽  
Particle Swarm ◽  
Ground Vibration ◽  
Open Pit ◽  
Swarm Optimization ◽  
Open Pit Mines ◽  
Computing Model

scholarly journals Application of the k - nearest neighbors algorithm for predicting blast - induced ground vibration in open - pit coal mines: a case study

2020 ◽  
Vol 61 (6) ◽  
pp. 22-29
Author(s):  
Hoang Nguyen . ◽  
Keyword(s):  
Artificial Intelligence ◽  
Coal Mine ◽  
Empirical Model ◽  
Ground Vibration ◽  
Nearest Neighbors ◽  
Superior Performance ◽  
Open Pit ◽  
K Nearest Neighbors ◽  
Open Pit Mines

Blasting is considered as one of the most effective methods for rock fragmentation in open - pit mines. However, its side effects are significant, especially blast - induced ground vibration. Therefore, this study aims to develop and apply artificial intelligence in predicting blast - induced ground vibration in open - pit mines. Indeed, the k - nearest neighbors (KNN) algorithm was taken into account and developed for predicting blast - induced ground vibration at the Deo Nai open - pit coal mine (Vietnam) as a case study. An empirical model (i.e., USBM) was also developed to compare with the developed KNN model aiming to highlight the advantage of the KNN model. Accordingly, 194 blasting events were collected and analyzed for this aim. This database was then divided into two parts, 80% for training and 20% for testing. The MinMax scale and 10 - fold cross - validation techniques were applied to improve the accuracy, as well as avoid overfitting of the KNN model. Root - mean - squared error (RMSE) and determination coefficient (R2) were used as the performance metrics for models’ evaluation and comparison purposes. The results indicated that the KNN model yielded better superior performance than those of the USBM empirical model with an RMSE of 1.157 and R2 of 0.967. In contrast, the USBM model only provided a weak performance with an RMSE of 4.205 and R2 of 0.416. With the obtained results, the KNN can be introduced as a potential artificial intelligence model for predicting and controlling blast - induced ground vibration in practical engineering, especially at the Deo Nai open - pit coal mine.

Prediction of Blast-Induced Ground Vibration Intensity in Open-Pit Mines Using Unmanned Aerial Vehicle and a Novel Intelligence System

2019 ◽  
Vol 29 (2) ◽  
pp. 771-790 ◽  
Author(s):  
Xuan-Nam Bui ◽  
Yosoon Choi ◽  
Victor Atrushkevich ◽  
Hoang Nguyen ◽  
Quang-Hieu Tran ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Ground Vibration ◽  
Open Pit ◽  
Open Pit Mines ◽  
Vibration Intensity ◽  
Intelligence System ◽  
Aerial Vehicle
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