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

Abstract

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.

Eco-Friendly Design and Sustainability Assessments of Fibre-Reinforced High-Strength Concrete Structures Automated by Data-Driven Machine Learning Models

“…The BNN and ANN models show better performance than Xgboost models during the testing phase, and BNN models have higher comprehensive predicting performance than other models in the prediction phase of five key structural and sustainable performance indicators. Similar results are shown in the work by Fissha, et al [59], which showed that the BNN models outperformed traditional neural networks with noisy datasets.…”

Explosive utilization efficiency enhancement: An application of machine learning for powder factor prediction using critical rock characteristics

“…Finally, the output nodes generate the results. Fissha [ 64 ] elucidated that the training of ANN entails the fine-tuning of weights in accordance with the network's performance in achieving desired outcomes, with the objective of maximizing accuracy. Several authors have embraced this strategy over the years.…”

Assessment of the ground vibration during blasting in mining projects using different computational approaches

“…These networks fit nonlinear and perceiving patterns 87 . Fissha et al 88 introduced the application of Bayesian-based ANN to improve the Mikurahana quarry blasting impact, demonstrating a new advantage of neural networks for ground improvement. Bhatawdekar et al 89 developed a soft computing model for estimating fly rock distance using different input variables, such as hole diameter, burden, stemming length, rock density, charge-per-meter, powder factor (PF), blast-ability index (BI), and weathering index datasets using hybrid ANN approaches.…”