title: A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network.
abstract: 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.
authors.affiliation: Department of Geotechnics and Transportation, Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, Malaysia.
authors.forename: Aminaton
authors.initials: A
authors.lastname: Marto
authors.affiliation: Construction Research Alliance, Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, Malaysia.
authors.forename: Mohsen
authors.identifier: 0000-0003-4186-9450
authors.initials: M
authors.lastname: Hajihassani
authors.affiliation: Department of Geotechnics and Transportation, Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, Malaysia.
authors.forename: Danial Jahed
authors.initials: DJ
authors.lastname: Armaghani
authors.affiliation: Department of Geotechnics and Transportation, Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, Malaysia.
authors.forename: Edy Tonnizam
authors.initials: ET
authors.lastname: Mohamad
authors.affiliation: Department of Structures and Materials, Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, Malaysia.
authors.forename: Ahmad Mahir
authors.initials: AM
authors.lastname: Makhtar
mesh_terms: D000465:Algorithms; D016571:Neural Networks, Computer