Artificial neural network or empirical criteria? A comparative approach in evaluating maximum charge per delay in surface mining — Sungun copper mine

2012 ◽  
Vol 79 (6) ◽  
pp. 652-658 ◽  
Author(s):  
A. Alipour ◽  
M. Mokhtarian ◽  
J. Abdollahei Sharif
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Surface Mining ◽  
Comparative Approach ◽  
Copper Mine ◽  
Sungun Copper Mine ◽  
Maximum Charge ◽  
Artificial Neural

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.

Comparative study on performance of different artificial neural network methods for prediction of the Covid19

foresight ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alireza Sedighi Fard
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Short Term Memory ◽  
Polynomial Regression ◽  
World Health ◽  
Comparative Approach ◽  
Data Set ◽  
Current Time ◽  
Content Type ◽  
Artificial Neural

Purpose This study aims to compare many artificial neural network (ANN) methods to find out which method is better for the prediction of Covid19 number of cases in N steps ahead of the current time. Therefore, the authors can be more ready for similar issues in the future. Design/methodology/approach The authors are going to use many ANNs in this study including, five different long short-term memory (LSTM) methods, polynomial regression (from degree 2 to 5) and online dynamic unsupervised feedforward neural network (ODUFFNN). The authors are going to use these networks over a data set of Covid19 number of cases gathered by World Health Organization. After 1,000 epochs for each network, the authors are going to calculate the accuracy of each network, to be able to compare these networks by their performance and choose the best method for the prediction of Covid19. Findings The authors concluded that for most of the cases LSTM could predict Covid19 cases with an accuracy of more than 85% after LSTM networks ODUFFNN had medium accuracy of 45% but this network is highly flexible and fast computing. The authors concluded that polynomial regression cant is a good method for the specific purpose. Originality/value Considering the fact that Covid19 is a new global issue, less studies have been conducted with a comparative approach toward the prediction of Covid19 using ANN methods to introduce the best model of the prediction of this virus.

scholarly journals Rotatable central composite design versus artificial neural network for modeling biosorption of Cr6+ by the immobilized Pseudomonas alcaliphila NEWG-2

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
WesamEldin I. A. Saber ◽  
Noura El-Ahmady El-Naggar ◽  
Mohammed S. El-Hersh ◽  
Ayman Y. El-khateeb ◽  
Ashraf Elsayed ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Central Composite Design ◽  
Immobilized Cells ◽  
Comparative Approach ◽  
Bacterial Cells ◽  
Ann Model ◽  
Biosorption Process ◽  
Artificial Neural ◽  
Central Composite

AbstractHeavy metals, including chromium, are associated with developed industrialization and technological processes, causing imbalanced ecosystems and severe health concerns. The current study is of supreme priority because there is no previous work that dealt with the modeling of the optimization of the biosorption process by the immobilized cells. The significant parameters (immobilized bacterial cells, contact time, and initial Cr6+ concentrations), affecting Cr6+ biosorption by immobilized Pseudomonas alcaliphila, was verified, using the Plackett–Burman matrix. For modeling the maximization of Cr6+ biosorption, a comparative approach was created between rotatable central composite design (RCCD) and artificial neural network (ANN) to choose the most fitted model that accurately predicts Cr6+ removal percent by immobilized cells. Experimental data of RCCD was employed to train a feed-forward multilayered perceptron ANN algorithm. The predictive competence of the ANN model was more precise than RCCD when forecasting the best appropriate wastewater treatment. After the biosorption, a new shiny large particle on the bead surface was noticed by the scanning electron microscopy, and an additional peak of Cr6+ was appeared by the energy dispersive X-ray analysis, confirming the role of the immobilized bacteria in the biosorption of Cr6+ ions.

Comparative approach for soil quality index based on spatial multi-criteria analysis and artificial neural network

2022 ◽  
Vol 15 (1) ◽  
Author(s):  
Sena Pacci ◽  
Nursaç Serda Kaya ◽  
İnci Demirağ Turan ◽  
Mehmet Serhat Odabas ◽  
Orhan Dengiz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Quality ◽  
Quality Index ◽  
Comparative Approach ◽  
Soil Quality Index ◽  
Artificial Neural ◽  
Multi Criteria Analysis

scholarly journals Comparison of Response Surface Methodology and Hybrid-Training Approach of Artificial Neural Network in Modeling the Properties of Concrete Containing Steel Fiber Extracted from Waste Tires

2019 ◽  
Author(s):  
Temitope F. Awolusi ◽  
Oluwaseyi L. Oke ◽  
Olufunke O. Akinkurolere ◽  
Olumoyewa Atoyebi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Steel Fiber ◽  
Fiber Reinforced Concrete ◽  
Comparative Approach ◽  
Split Tensile Strength ◽  
Training Approach ◽  
Artificial Neural

The study presents a comparative approach between response surface methodology (RSM) and hybridized, genetic algorithm artificial neural network (GA-ANN) in predicting the water absorption, compressive strength, flexural strength split tensile strength and slump for steel fiber reinforced concrete. The effect of process variables such as aspect ratio, water cement ratio and cement content were investigated using the central composite design of response surface methodology.  This same experimental design was used in training the hybrid-training approach of artificial neural network. The predicting ability of both methodologies were compared using the  root mean sqaured error (RMSE), mean absolute error (MAE), model predictive error (MPE) and absolute average deviation (AAD). The RSM model was found more accurate in prediction compared to hybrid GA-ANN.

scholarly journals A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Aminaton Marto ◽  
Mohsen Hajihassani ◽  
Danial Jahed Armaghani ◽  
Edy Tonnizam Mohamad ◽  
Ahmad Mahir Makhtar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Imperialist Competitive Algorithm ◽  
Ann Model ◽  
Competitive Algorithm ◽  
Maximum Charge ◽  
Flyrock Distance ◽  
Novel Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Flyrock is one of the major disturbances induced by blasting which may cause severe damage to nearby structures. This phenomenon has to be precisely predicted and subsequently controlled through the changing in the blast design to minimize potential risk of blasting. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of imperialist competitive algorithm (ICA) and artificial neural network (ANN). For this purpose, the parameters of 113 blasting operations were accurately recorded and flyrock distances were measured for each operation. By applying the sensitivity analysis, maximum charge per delay and powder factor were determined as the most influential parameters on flyrock. In the light of this analysis, two new empirical predictors were developed to predict flyrock distance. For a comparison purpose, a predeveloped backpropagation (BP) ANN was developed and the results were compared with those of the proposed ICA-ANN model and empirical predictors. The results clearly showed the superiority of the proposed ICA-ANN model in comparison with the proposed BP-ANN model and empirical approaches.

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