Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration

Rock blasting is one of the most common and cost-effective excavation techniques. However, rock blasting has various negative environmental effects, such as air overpressure, fly rock, and ground vibration. Ground vibration is the most hazardous of these inevitable impacts since it has a negative impact not only on the environment of the surrounding area but also on the human population and the rock itself. The PPV is the most critical base parameter practice for understanding, evaluating, and predicting ground vibration in terms of vibration velocity. This study aims to predict the blast-induced ground vibration of the Mikurahana quarry, using Bayesian neural network (BNN) and four machine learning techniques, namely, gradient boosting, k-neighbors, decision tree, and random forest. The proposed models were developed using eight input parameters, one output, and one hundred blasting datasets. The assessment of the suitability of one model in comparison to the others was conducted by using different performance evaluation metrics, such as R, RMSE, and MSE. Hence, this study compared the performances of the BNN model with four machine learning regression analyses, and found that the result from the BNN was superior, with a lower error: R = 0.94, RMSE = 0.17, and MSE = 0.03. Finally, after the evaluation of the models, SHAP was performed to describe the importance of the models' features and to avoid the black box issue.

Automated summary

Rock blasting is a commonly used and cost-effective excavation technique, but it has negative environmental effects such as air overpressure, fly rock, and ground vibration. Ground vibration is the most detrimental impact, affecting both the environment and the human population. This study used Bayesian neural network and four machine learning techniques to predict blast-induced ground vibration. The evaluation of models showed that the BNN model outperformed the others with lower error: R = 0.94, RMSE = 0.17, and MSE = 0.03. SHAP analysis was also performed to explain the importance of model features and address the black box issue.