Vol.:(0123456789) 1 3 Engineering with Computers https://doi.org/10.1007/s00366-018-0686-3 ORIGINAL ARTICLE Novel approach to predicting blast-induced ground vibration using Gaussian process regression Clement Kweku Arthur 1  · Victor Amoako Temeng 1  · Yao Yevenyo Ziggah 2 Received: 25 August 2018 / Accepted: 14 December 2018 © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract An attempt has been made to propose a novel prediction model based on the Gaussian process regression (GPR) approach. The proposed GPR was used to predict blast-induced ground vibration using 210 blasting events from an open pit mine in Ghana. Out of the 210 blasting data, 130 were used in the model development (training), whereas the remaining 80 were used to independently assess the performance of the GPR model. The formulated GPR model was compared with the other standard predictive techniques such as the generalised regression neural network, radial basis function neural network, back-propagation neural network, and four conventional ground vibration predictors (United State Bureau of Mines model, Langefors and Kihlstrom model, Ambraseys–Hendron model, and Indian Standard model). Comparatively, the statistical results revealed that the proposed GPR approach can predict ground vibration more accurately than the standard techniques presented in this study. The GPR had the highest correlation coefcient (R), variance accounted for, and the lowest values of the statistical error indicators (mean absolute error and root-mean-square error) applied. The superiority of GPR to the other methods is explained by the ability of the GPR to quantitatively model the noise patterns in the blasting data events adequately. The study will serve as a foundation for future research works in the mining industry where artifcial intelligence technology is yet to be fully explored. Keywords Gaussian process regression · Artifcial neural network · Ground vibration empirical predictors · Blasting 1 Introduction The Earth is richly endowed with mineral reserves (raw materials) which are benefcial to the existence of mankind. These raw materials include precious metals such as gold, diamond, silver, bauxite, iron, nickel, manganese, cobalt, platinum, vermiculite, and zirconium. However, these miner- als are buried deep down the Earth and, hence, surrounded by a large massive waste rock formation. To have access to these minerals and make it available to mankind, the process of mining is usually employed. Mining is conventionally done through drill and blast operation through which inclined or vertical holes are drilled into the rock formation. Explosives are then used to frag- ment the rock mass into smaller pieces, thereby creating shock waves in the drilled holes. The blasting event leads to a high chemical reaction which evolves a huge quantity of energy which starts propagating away in a radial direction. Initially, the intensity of the energy is so high that matter near the walls of the blast holes are crushed and displaced radially. However, as the energy intensity decreases, due to geometric spreading, the energy continues to travel through the in situ rock mass as an elastic ground vibration [1, 2]. The unused energy in fragmenting the in situ rock mass also generates other undesirable efects such as fyrock, noise, air overpressure, and backbreak [3–6]. Moreover, blast-induced ground vibration which is the focus of this study, could cause structural responses and nuisance to humans [7, 8]. In the light of that, it has become a mandatory responsibility of every mining company to monitor the levels of ground vibra- tion during each blast event. This monitoring will provide management of the mine to have the frst-hand information * Victor Amoako Temeng vatemeng@umat.edu.gh 1 Department of Mining Engineering, Faculty of Mineral Resources Technology, University of Mines and Technology, Tarkwa, Western Region, Ghana 2 Department of Geomatic Engineering, Faculty of Mineral Resources Technology, University of Mines and Technology, Tarkwa, Western Region, Ghana