fragmentation prediction
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2019 ◽  
Vol 18 (6) ◽  
pp. 2385-2396
Author(s):  
Ufuk Kirik ◽  
Jan C. Refsgaard ◽  
Lars J. Jensen


2018 ◽  
Vol 2 (3) ◽  
Author(s):  
Danial Jahed Armaghani

Reliable estimation of rock fragmentation is an important issue in the blasting operations in order to predict quality of the production. Since rock fragmentation is affected by various parameters such as blast pattern and rock mass characteristics, it is very difficult to have an appreciate prediction of it. This paper describes a new hybrid imperialism competitive algorithm (ICA)-artificial neural network (ANN) in order to solve shortcomings of ANN itself for prediction of rock fragmentation. In fact, the influence of ICA on ANN results was studied in this research. By investigating the related studies, the most important parameters of ICA were identified and a series of parametric studies for their determination were conducted. All models were built using 8 inputs and one output which is rock fragmentation. To have a fair comparison and show the capability of the new hybrid model, a pre-developed ANN model was also considered and constructed. Evaluation of the obtained results demonstrated that a higher ability of rock fragmentation prediction is received developing a hybrid ICA-ANN model. Coefficient of determination (R2) values of (0.949 and 0.813) and (0.941 and 0.819) were obtained for training and testing of ICA-ANN and ANN models, respectively which indicated that the proposed ICA-ANN model can be implemented better in improving performance capacity of ANN model in estimating rock fragmentation.



Author(s):  
Danial Jahed Armaghani

Reliable estimation of rock fragmentation is an important issue in the blasting operations in order to predict quality of the production. Since rock fragmentation is affected by various parameters such as blast pattern and rock mass characteristics, it is very difficult to have an appreciate prediction of it. This paper describes a new hybrid imperialism competitive algorithm (ICA)-artificial neural network (ANN) in order to solve shortcomings of ANN itself for prediction of rock fragmentation. In fact, the influence of ICA on ANN results was studied in this research. By investigating the related studies, the most important parameters of ICA were identified and a series of parametric studies for their determination were conducted. All models were built using 8 inputs and one output which is rock fragmentation. To have a fair comparison and show the capability of the new hybrid model, a pre-developed ANN model was also considered and constructed. Evaluation of the obtained results demonstrated that a higher ability of rock fragmentation prediction is received developing a hybrid ICA-ANN model. Coefficient of determination (R2) values of (0.949 and 0.813) and (0.941 and 0.819) were obtained for training and testing of ICA-ANN and ANN models, respectively which indicated that the proposed ICA-ANN model can be implemented better in improving performance capacity of ANN model in estimating rock fragmentation.



2017 ◽  
Vol 19 (22) ◽  
pp. 6252-6255 ◽  
Author(s):  
Daeyeon Jeong ◽  
Te-ik Sohn ◽  
Jong Yup Kim ◽  
Gyudong Kim ◽  
Deukjoon Kim ◽  
...  


2012 ◽  
Vol 6 (9) ◽  
pp. 3319-3329 ◽  
Author(s):  
Mostafa Badroddin ◽  
Ezzeddin Bakhtavar ◽  
Hasan Khoshrou ◽  
Bahram Rezaei


2010 ◽  
Vol 2 (6) ◽  
pp. 259-270 ◽  
Author(s):  
Elli Tyrkkö ◽  
Anna Pelander ◽  
Ilkka Ojanperä


2009 ◽  
Vol 1 (1) ◽  
pp. 585-591 ◽  
Author(s):  
Peng Shao ◽  
Zhi-wei Xu ◽  
Hou-quan Zhang ◽  
Yong-nian He


Fragblast ◽  
2000 ◽  
Vol 4 (3-4) ◽  
pp. 198-207 ◽  
Author(s):  
Stephen H. Chung ◽  
P.D. Katsabanis


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