Feasibility of indirect determination of blast induced ground vibration based on support vector machine. (November 2015)

• Record Type: Journal Article
• Authors: Hasanipanah, Mahdi; Monjezi, Masoud; Shahnazar, Azam; Jahed Armaghani, Danial; Farazmand, Alireza
• Is Part Of: Measurement. Volume 75(2015:Nov.)
• Page Start: 289
• Page End: 297
• Publication Date: 2015-11
• Subjects: Blasting -- Peak particle velocity -- Empirical equation -- Support vector machine; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2015.07.019
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: We measured data of 80 blasting operations in Bakhtiari Dam, Iran. We proposed a SVM model for predicting PPV resulting from blasting. Empirical equations were also employed to predict PPV. A comparison was made to demonstrate capability of the proposed models. Abstract: Mines, quarries, and construction sites face blasting environmental problems due to high level of ground vibrations. This phenomena can cause injury to both human and damage to structures in the blasting environment. To estimate ground vibration, several empirical predictors have been established by various researchers, while these predictors are not commonly enforceable beyond the particular conditions. However, ground vibration prediction is a complicated issue in consequence of the fact that a large number of influential factors are involved. In this study, a support vector machine (SVM) was applied and developed to predict ground vibration in blasting operations of Bakhtiari Dam, Iran. To achieve this aim, 80 blasting works were investigated and results of peak particle velocity (PPV) as a vibration index, distance from the blast-face and maximum charge per delay were measured and monitored to utilize in the modeling. To demonstrate applicability of the SVM model for prediction of PPV, several empirical equations were also employed and the relevant site constants were proposed. In the analyses procedure of this study, 60 datasets were used for model development and remaining 20 datasets were applied to check the performance capacity of the developed model. After comparing the results obtained from SVM and empirical equations, it was found that the SVM method provides higher performance capacity in predicting PPV compared to empirical equations.