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A Bayesian Approach to Predict Blast-Induced Damage of High Rock Slope Using Vibration and Sonic Data
The blast-induced damage of a high rock slope is directly related to construction safety and the operation performance of the slope. Approaches currently used to measure and predict the blast-induced damage are time-consuming and costly. A Bayesian approach was proposed to predict the blast-induced...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8038205/ https://www.ncbi.nlm.nih.gov/pubmed/33918354 http://dx.doi.org/10.3390/s21072473 |
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author | Sun, Pengchang Lu, Wenbo Hu, Haoran Zhang, Yuzhu Chen, Ming Yan, Peng |
author_facet | Sun, Pengchang Lu, Wenbo Hu, Haoran Zhang, Yuzhu Chen, Ming Yan, Peng |
author_sort | Sun, Pengchang |
collection | PubMed |
description | The blast-induced damage of a high rock slope is directly related to construction safety and the operation performance of the slope. Approaches currently used to measure and predict the blast-induced damage are time-consuming and costly. A Bayesian approach was proposed to predict the blast-induced damage of high rock slopes using vibration and sonic data. The relationship between the blast-induced damage and the natural frequency of the rock mass was firstly developed. Based on the developed relationship, specific procedures of the Bayesian approach were then illustrated. Finally, the proposed approach was used to predict the blast-induced damage of the rock slope at the Baihetan Hydropower Station. The results showed that the damage depth representing the blast-induced damage is proportional to the change in the natural frequency. The first step of the approach is establishing a predictive model by undertaking Bayesian linear regression, and the second step is predicting the damage depth for the next bench blasting by inputting the change rate in the natural frequency into the predictive model. Probabilities of predicted results being below corresponding observations are all above 0.85. The approach can make the best of observations and includes uncertainty in predicted results. |
format | Online Article Text |
id | pubmed-8038205 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80382052021-04-12 A Bayesian Approach to Predict Blast-Induced Damage of High Rock Slope Using Vibration and Sonic Data Sun, Pengchang Lu, Wenbo Hu, Haoran Zhang, Yuzhu Chen, Ming Yan, Peng Sensors (Basel) Article The blast-induced damage of a high rock slope is directly related to construction safety and the operation performance of the slope. Approaches currently used to measure and predict the blast-induced damage are time-consuming and costly. A Bayesian approach was proposed to predict the blast-induced damage of high rock slopes using vibration and sonic data. The relationship between the blast-induced damage and the natural frequency of the rock mass was firstly developed. Based on the developed relationship, specific procedures of the Bayesian approach were then illustrated. Finally, the proposed approach was used to predict the blast-induced damage of the rock slope at the Baihetan Hydropower Station. The results showed that the damage depth representing the blast-induced damage is proportional to the change in the natural frequency. The first step of the approach is establishing a predictive model by undertaking Bayesian linear regression, and the second step is predicting the damage depth for the next bench blasting by inputting the change rate in the natural frequency into the predictive model. Probabilities of predicted results being below corresponding observations are all above 0.85. The approach can make the best of observations and includes uncertainty in predicted results. MDPI 2021-04-02 /pmc/articles/PMC8038205/ /pubmed/33918354 http://dx.doi.org/10.3390/s21072473 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sun, Pengchang Lu, Wenbo Hu, Haoran Zhang, Yuzhu Chen, Ming Yan, Peng A Bayesian Approach to Predict Blast-Induced Damage of High Rock Slope Using Vibration and Sonic Data |
title | A Bayesian Approach to Predict Blast-Induced Damage of High Rock Slope Using Vibration and Sonic Data |
title_full | A Bayesian Approach to Predict Blast-Induced Damage of High Rock Slope Using Vibration and Sonic Data |
title_fullStr | A Bayesian Approach to Predict Blast-Induced Damage of High Rock Slope Using Vibration and Sonic Data |
title_full_unstemmed | A Bayesian Approach to Predict Blast-Induced Damage of High Rock Slope Using Vibration and Sonic Data |
title_short | A Bayesian Approach to Predict Blast-Induced Damage of High Rock Slope Using Vibration and Sonic Data |
title_sort | bayesian approach to predict blast-induced damage of high rock slope using vibration and sonic data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8038205/ https://www.ncbi.nlm.nih.gov/pubmed/33918354 http://dx.doi.org/10.3390/s21072473 |
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