title: Prediction of blast-induced air overpressure: a hybrid AI-based predictive model.
abstract: Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of blast operations which needs to be predicted to minimize the potential risk of damage. This paper presents an artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) for the prediction of AOp induced by quarry blasting. For this purpose, 95 blasting operations were precisely monitored in a granite quarry site in Malaysia and AOp values were recorded in each operation. Furthermore, the most influential parameters on AOp, including the maximum charge per delay and the distance between the blast-face and monitoring point, were measured and used to train the ICA-ANN model. Based on the generalized predictor equation and considering the measured data from the granite quarry site, a new empirical equation was developed to predict AOp. For comparison purposes, conventional ANN models were developed and compared with the ICA-ANN results. The results demonstrated that the proposed ICA-ANN model is able to predict blast-induced AOp more accurately than other presented techniques.
authors.affiliation: Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, Johor, Malaysia. danialarmaghani@gmail.com.
authors.forename: Danial
authors.initials: D
authors.lastname: Jahed Armaghani
authors.affiliation: Construction Research Alliance, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, Johor, Malaysia. mohsen_hajihassani@yahoo.com.
authors.forename: Mohsen
authors.initials: M
authors.lastname: Hajihassani
authors.affiliation: Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, Johor, Malaysia. aminaton@utm.my.
authors.forename: Aminaton
authors.initials: A
authors.lastname: Marto
authors.affiliation: Young Researchers and Elit Club, South Tehran Branch, Islamic Azad University, Tehran, Iran. roohollah.shirani@modares.ac.ir.
authors.forename: Roohollah
authors.initials: R
authors.lastname: Shirani Faradonbeh
authors.affiliation: Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, Johor, Malaysia. edy@utm.my.
authors.forename: Edy Tonnizam
authors.initials: ET
authors.lastname: Mohamad
mesh_terms: D000465:Algorithms; D004784:Environmental Monitoring / Q000379:methods*; D005107:Explosions; D008296:Malaysia; D008962:Models, Theoretical; D016571:Neural Networks, Computer
keywords: ANN; Air overpressure; Blast operation; Environmental impacts; ICA