A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure. (October 2016)

• Record Type: Journal Article
• Authors: Amiri, Maryam; Bakhshandeh Amnieh, Hassan; Hasanipanah, Mahdi; Mohammad Khanli, Leyli
• Is Part Of: Engineering with computers. Volume 32:Number 4(2016)
• Page Start: 631
• Page End: 644
• Publication Date: 2016-10
• Subjects: Blasting operation -- Ground vibration -- Air-overpressure -- Artificial neural network -- K-nearest neighbors; 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/s00366-016-0442-5
• Languages: English
• ISSNs: 0177-0667

Abstract

Abstract Blasting operation is widely used method for rock excavation in mining and civil works. Ground vibration and air-overpressure (AOp) are two of the most detrimental effects induced by blasting. So, evaluation and prediction of ground vibration and AOp are essential. This paper presents a new combination of artificial neural network (ANN) andK -nearest neighbors (KNN) models to predict blast-induced ground vibration and AOp. Here, this combination is abbreviated using ANN-KNN. To indicate performance of the ANN-KNN model in predicting ground vibration and AOp, a pre-developed ANN as well as two empirical equations, presented by United States Bureau of Mines (USBM), were developed. To construct the mentioned models, maximum charge per delay (MC) and distance between blast face and monitoring station (D ) were set as input parameters, whereas AOp and peak particle velocity (PPV), as a vibration index, were considered as output parameters. A database consisting of 75 datasets, obtained from the Shur river dam, Iran, was utilized to develop the mentioned models. In terms of using three performance indices, namely coefficient correlation (R 2 ), root mean square error and variance account for, the superiority of the ANN-KNN model was proved in comparison with the ANN and USBM equations.