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Hokkaido University Collection of Scholarly and Academic Papers >
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Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration
| Title: | Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration |
| Authors: | Fissha, Yewuhalashet Browse this author | | Ikeda, Hajime Browse this author | | Toriya, Hisatoshi Browse this author | | Adachi, Tsuyoshi Browse this author | | Kawamura, Youhei Browse this author →KAKEN DB |
| Keywords: | blasting | | ground vibration | | ppv | | Bayesian neural network | | machine learning regression |
| Issue Date: | 1-Mar-2023 |
| Publisher: | MDPI |
| Journal Title: | Applied sciences |
| Volume: | 13 |
| Issue: | 5 |
| Start Page: | 3128 |
| Publisher DOI: | 10.3390/app13053128 |
| 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. |
| Rights: | © 2023 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/). |
| Type: | article |
| URI: | http://hdl.handle.net/2115/88883 |
| Appears in Collections: | 工学院・工学研究院 (Graduate School of Engineering / Faculty of Engineering) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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