Morena, Badisheng Isaac2022-05-042022-05-042019Morena, Badisheng Isaac. (2019). Prediction of blast vibrations from quarries using machine learning algorithms and empirical formulae. University of the Witwatersrand, https://hdl.handle.net/10539/32862https://hdl.handle.net/10539/32862A research report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science in Engineering.The aim of this study was to, firstly, use machine learning algorithms to predict Peak Particle Velocity (PPV) in order to optimise blasting layouts and reduce the risk of damaging surface structures. Empirical models developed by the United States Bureau of Mines (USBM) (1963) and Ambraseys and Hendron (1968) were compared to the machine learning algorithms. The tests conducted were interpolation and extrapolation. Most of the data used in this report was obtained from the USBM’s Bulletin 656. The data was analysed using a qualitative and quantitative research methods. The Cubist machine learning model (Kuhn, 2018) performed the best in the interpolation test with a coefficient of determination (R2) of 83.39 % and a root mean squared error (RMSE) and mean absolute error (MAE) of 10.64 and 7.30 respectively. The empirical models performed the best with the extrapolation test with an average R2 of 88 % and RMSE and MAE of 9.17 and 6.59 respectively. This research has shown the effectiveness of machine algorithms in predicting PPV and empirical formulae using historical data from different sites. However, explosive and geotechnical information was not available in the dataset and it is therefore recommended that further research be conducted with this data.Online resource (xi, 252 leaves)enMachine learningAlgorithmsThesis