A combination of the ICA-ANN model to predict air-overpressure resulting from blasting. (January 2016)

• Record Type: Journal Article
• Authors: Jahed Armaghani, Danial; Hasanipanah, Mahdi; Tonnizam Mohamad, Edy
• Is Part Of: Engineering with computers. Volume 32:Number 1(2016)
• Page Start: 155
• Page End: 171
• Publication Date: 2016-01
• Subjects: Blasting -- Air-overpressure -- Artificial neural network -- Imperialist competitive algorithm; Engineering design -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Conception technique -- Informatique -- Périodiques; Conception assistée par ordinateur -- Périodiques; Electronic journals; 620.00285
• DOI: 10.1007/s00366-015-0408-z
• Languages: English
• ISSNs: 0177-0667

Abstract

Abstract Blasting operations usually produce significant environmental problems which may cause severe damage to the nearby areas. Air-overpressure (AOp) is one of the most important environmental impacts of blasting operations which needs to be predicted and subsequently controlled to minimize the potential risk of damage. This paper presents three non-linear methods, namely empirical, artificial neural network (ANN), and imperialist competitive algorithm (ICA)-ANN to predict AOp induced by blasting operations in Shur river dam, Iran. ICA as a global search population-based algorithm can be used to optimize the weights and biases of the network connection for training by ANN. In this study, 70 blasting operations were investigated and relevant blasting parameters were measured. The most influential parameters on AOp, namely maximum charge per delay and the distance from the blast-face, were considered as input parameters or predictors. Using the five randomly selected datasets and considering the modeling procedure of each method, 15 models were constructed for all predictive techniques. Several performance indices including coefficient of determination (R 2 ), root mean square error and value account for were utilized to check the performance capacity of the predictive methods. Considering these performance indices and using simple ranking method, the best models were selected among all constructed models. It was found that the ICA-ANN approach can provide higher performance capacity in predicting AOp compared to other predictive methods.