A novel nonlinear modeling for the prediction of blast-induced airblast using a modified conjugate FR method. (January 2019)

• Record Type: Journal Article
• Authors: Keshtegar, Behrooz; Hasanipanah, Mahdi; Bakhshayeshi, Iman; Esfandi Sarafraz, Mehdi
• Is Part Of: Measurement. Volume 131(2019)
• Page Start: 35
• Page End: 41
• Publication Date: 2019-01
• Subjects: Blasting operation -- Airblast -- Modified conjugate FR method -- Nonlinear modeling -- USBM; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2018.08.052
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Prediction of blast-induced airblast in Shur river dam region, Iran, using 81 datasets. Developing eight nonlinear predictive models based on modified conjugate FR method. A comparison was made to demonstrate capability of the proposed models. Abstract: Prediction of the blast-induced is an important issue in Shur river dam, Iran. The nonlinear mathematical models can provide an appropriate flexibility to achieve the accurate predictions of the blast-induced airblast. In this paper, a set of nonlinear mathematical models with eight empirical relations, which are added based on the logarithmic and power basic functions, are selected to calibrate of the mine blasting airblast using two input variables, i.e. maximum charge per delay (MC) and distance from the blast-point (DI). A general regression analysis is proposed to calibrate the nonlinear models using a modified conjugate Fletcher and Reeves (FR) method using a limited scalar factor and dynamic step size to achieve the stabilization in nonlinear modeling. Finally, three simple empirical models are chosen to implement the prediction of the blast-induced airblast. The proposed empirical models were compared with the United States Bureau of Mines (USBM) model using several error statistics. The results indicate that the proposed modified FR model provides an appropriate calibration for the nonlinear regression analysis. Also, it was found that the empirical model proposed in this study, with the root mean square error (RMSE) of 3.79, is more accurate than USBM, with the RMSE of 4.22, and can be applied to other sites for predicting the airblast.