Predicting the ground vibration induced by mine blasting using imperialist competitive algorithm. Issue 4 (11th June 2018)

• Record Type: Journal Article
• Authors: Behzadafshar, Katayoun; Mohebbi, Fahimeh; Soltani Tehrani, Mehran; Hasanipanah, Mahdi; Tabrizi, Omid
• Is Part Of: Engineering computations. Volume 35:Issue 4(2018)
• Page Start: 1774
• Page End: 1787
• Publication Date: 2018-06-11
• Subjects: ANN -- Blasting -- Imperialist competitive algorithm -- PPV; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-08-2017-0290
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: The purpose of this paper is to propose three imperialist competitive algorithm (ICA)-based models for predicting the blast-induced ground vibrations in Shur River dam region, Iran. Design/methodology/approach: For this aim, 76 data sets were used to establish the ICA-linear, ICA-power and ICA-quadratic models. For comparison aims, artificial neural network and empirical models were also developed. Burden to spacing ratio, distance between shot points and installed seismograph, stemming, powder factor and max charge per delay were used as the models' input, and the peak particle velocity (PPV) parameter was used as the models' output. Findings: After modeling, the various statistical evaluation criteria such as coefficient of determination ( R 2) were applied to choose the most precise model in predicting the PPV. The results indicate the ICA-based models proposed in the present study were more acceptable and reliable than the artificial neural network and empirical models. Moreover, ICA linear model with the R 2 of 0.939 was the most precise model for predicting the PPV in the present study. Originality/value: In the present paper, the authors have proposed three novel prediction methods based on ICA to predict the PPV. In the next step, we compared the performance of the proposed ICA-based models with the artificial neural network and empirical models. The results indicated that the ICA-based models proposed in the present paper were superior in terms of high accuracy and have the capacity to generalize.