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

Aladejare Adeyemi Emman, Lawal Abiodun Ismail, Onifade Moshood

Acta Geophysica

2022

Vol. 70, no 2

581--591

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

Prediction of the blast induced ground vibration in tunnel blasting using ANN, moth fame optimized ANN, and gene expression programming

Lawal Abiodun Ismail, Kwon Sangki, Kim Geon Young

Acta Geophysica

2021

Vol. 69, no. 1

161--174

The blast-induced ground vibration (BIGV) is a severe environmental impact of blasting as it can afect the integrity of the structures and cause civil unrest. In this study, the BIGV of Daejeon tunnel was predicted taking into consideration parameters such as hole length, the charge per delay, number of holes, total charge, distance from the measuring station to the blasting point and the rock mass rating as the input parameters, while the peak particle velocity (PPV) was the targeted output parameter. An artifcial neural network (ANN) model was frst simulated. The optimum ANN structure obtained was optimized using a novel moth-fame optimization algorithm (MFO). The gene expression program (GEP) was also used to develop another new model. The proposed models were compared with the multilinear regression (MLR) model and the selected empirical models for the PPV predictions. The performance of the proposed model was evaluated using statistical indices such as adjusted coefcient of determination (adj R2 ), mean square error (MSE), mean absolute error (MAE), and the variance accounted for (VAF). The proposed MFO-ANN outperformed other models with the adj R2 of 0.9702 and 0.9577, VAF of 97.0472 and 95.9832, MSE of 0.0009 and 0.0008, and MAE of 0.0233 and 0.0216 for the respective training and testing phases. The sensitivity analysis was conducted using the weight partitioning method (WPM), and the charge per delay has the highest infuence on the predicted PPV. This study indicates the suitability of the proposed models for the prediction of PPV.