Paper Info
Title
An optimized ANN model based on genetic algorithm for predicting ripping production.
Abstract
Due to the environmental constraints and the limitations on blasting, ripping as a ground loosening and breaking method has become more popular in both mining and civil engineering applications. As a result, a more applicable rippability model is required to predict ripping production (Q) before conducting such tests. In this research, a hybrid genetic algorithm (GA) optimized by artificial neural network (ANN) was developed to predict ripping production results obtained from three sites in Johor state, Malaysia. It should be noted that the mentioned hybrid model was first time applied in this field. In this regard, 74 ripping tests were investigated in the studied areas and the relevant parameters were also measured. A series of GA–ANN models were conducted in order to propose a hybrid model with a higher accuracy level. To demonstrate the performance capacity of the hybrid GA–ANN model, a pre-developed ANN model was also proposed and results of predictive models were compared using several performance indices. The results revealed higher accuracy of the proposed hybrid GA–ANN model in estimating Q compared to ANN technique. As an example, root-mean-square error values of 0.092 and 0.131 for testing datasets of GA–ANN and ANN techniques, respectively, express the superiority of the newly developed model in predicting ripping production.
Year
DOI
Venue
2017
10.1007/s00521-016-2359-8
Neural Computing and Applications
Keywords
Field
DocType
Ripping production, ANN, GA–ANN, Hybrid model
Rippability,Rock blasting,Artificial intelligence,Artificial neural network,Machine learning,Genetic algorithm,Mathematics
Journal
Volume
Issue
ISSN
28
S-1
1433-3058
Citations 
PageRank 
References 
22
0.92
13
Authors
5
Name
Order
Citations
PageRank
Edy Tonnizam Mohamad118110.85
roohollah shirani faradonbeh21409.21
danial jahed armaghani358536.46
M. Monjezi427119.36
Muhd Zaimi Abd Majid51275.63