Predicting the peak particle velocity from rock blasting operations using Bayesian approach

Abstract Measuring the blast-induced ground vibration at blasting sites is very important, to plan and avoid adverse effects of in terms peak particle velocity (PPV). However, measurement PPV often requires time, cost, logistic commitment, which may not be economical for small-scale mining operations. This has prompted development numerous regression equations literature estimate from a relatively easier scaled distance (SD) measurement. With available literature, there challenge how select appropriate model specific site, more so that rocks behave differently site because different geological processes are subjected to. study develops method selects models by comparing evidence occurrence probability models. The with highest given SD data. selected then integrated prior knowledge data Bayesian framework probabilistic characterization PPV. opencast coal mine, Jharia coalfield Dhanbad district Jharkhand, India, used illustrate validate approach. mean standard deviation simulated samples proposed approach 12.38 mm/s 7.36 mm/s, respectively, close 12.03 9.24 estimated measured site. In addition, distribution consistent