Artificial Neural Network and Firefly Algorithm for Estimation and Minimization of Ground Vibration Induced by Blasting in a Mine

2020 ◽  
Vol 29 (6) ◽  
pp. 4121-4132 ◽  
Author(s):  
Parichehr Bayat ◽  
Masoud Monjezi ◽  
Mojtaba Rezakhah ◽  
Danial Jahed Armaghani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Firefly Algorithm ◽  
Ground Vibration ◽  
Artificial Neural

scholarly journals Optimization of Artificial Neural Network for Stock Market Price Prediction Using an Enhanced Firefly Algorithm

2019 ◽  
Vol 13 (9) ◽  
pp. 532-543
Author(s):  
Ameen Ahmed Oloduowo ◽  
Fashoto Stephen Gbenga ◽  
Ogeh Clement ◽  
Balogun Abdullateef ◽  
Mashwama Petros
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Market ◽  
Firefly Algorithm ◽  
Market Price ◽  
Price Prediction ◽  
Artificial Neural

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.

scholarly journals Blasting Vibration Control Using an Improved Artificial Neural Network in the Ashele Copper Mine

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shida Xu ◽  
Tianxiao Chen ◽  
Jiaqi Liu ◽  
Chenrui Zhang ◽  
Zhiyang Chen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ground Vibration ◽  
Principal Component ◽  
Blasting Vibration ◽  
Horizontal Distance ◽  
Ann Model ◽  
Negative Effects ◽  
Maximum Charge ◽  
Artificial Neural

Blasting is currently the most important method for rock fragmentation in metal mines. However, blast-induced ground vibration causes many negative effects, including great damage to surrounding rock masses and projects and even casualties in severe cases. Therefore, prediction of the peak particle velocity (PPV) caused by blasting plays an important role in reducing safety threats. In this paper, a genetic algorithm (GA) and an artificial neural network (ANN) algorithm were jointly used to construct a neural network model with a 4-5-1 topology to predict the PPV. For this model, the ANN parameters were optimized using the GA, and the deviating direction, horizontal distance, vertical distance, Euclidean distance, explosive type, burden, hole spacing, and maximum charge per delay were used as input information. Moreover, principal component analysis (PCA) was used to extract the first four principal components from the eight input factors as the four inputs of the ANN model. The model was successfully applied to protect an underground crushing cave from blasting vibration damage by adjusting the blasting parameters. Compared with several widely used empirical equations, the GA-ANN PPV prediction model produced significantly better results, while the Ambraseys–Hedron method was the best of the empirical methods. Therefore, the improved GA-ANN model can be used to predict the PPV on site and provide a reference for the control of blasting vibration in field production.

Prediction of blast-induced ground vibration using empirical models and artificial neural network (Bakhtiari Dam access tunnel, as a case study)

2019 ◽  
Vol 26 (7-8) ◽  
pp. 520-531 ◽  
Author(s):  
Ali M Rajabi ◽  
Alireza Vafaee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Ground Vibration ◽  
Ground Vibrations ◽  
Blasting Operation ◽  
Access Road ◽  
Artificial Neural

Blasting operation is among the most common methods of rock excavation in the civil engineering and mining operations. Ground vibration is the most unfavorable effect of blasting operation such that failure to accurately control this problem causes damage to adjacent structures. In this regard, geotechnical engineers face the challenge of accurately predicting blast-induced ground vibrations. Geographical location of Bakhtiari Dam (located in the southwest of Iran) is needed to construct an access road to its nearest city through the rough topography. To establish the access road in the plan, blasting operation methods have been used. In this study, blast-induced ground vibrations in the study area are evaluated using five common functional forms of the empirical model and their corrected regression coefficient for the area. Then, the ground vibrations generated in the study area were predicted by designing an artificial neural network model. For this purpose, the maximum charge per delay, the distance between the blast point and monitoring stations, and the ground vibration values were surveyed for 80 blast events, and their necessary parameters were determined. A total of 64 datasets were used to obtain the coefficients of the empirical models and to create the artificial neural network model. In addition, 16 datasets were used to estimate the performance and accuracy of each model. To measure the accuracy of the constructed models, some statistical parameters were also used. The results show that in the study area, the artificial neural network model presents the most accurate and appropriate model for predicting blast-induced ground vibrations. The neural network proposed in this research is suggested for areas with geological features resembling those of the present study.

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