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...
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| Format: | Article |
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MDPI AG
2021-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/7/2473 |
| _version_ | 1797538940808331264 |
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| author | Pengchang Sun Wenbo Lu Haoran Hu Yuzhu Zhang Ming Chen Peng Yan |
| author_facet | Pengchang Sun Wenbo Lu Haoran Hu Yuzhu Zhang Ming Chen Peng Yan |
| author_sort | Pengchang Sun |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T12:38:19Z |
| format | Article |
| id | doaj.art-0e864223ffe04678946d1ece3635d8e7 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T12:38:19Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0e864223ffe04678946d1ece3635d8e72023-11-21T14:01:36ZengMDPI AGSensors1424-82202021-04-01217247310.3390/s21072473A Bayesian Approach to Predict Blast-Induced Damage of High Rock Slope Using Vibration and Sonic DataPengchang Sun0Wenbo Lu1Haoran Hu2Yuzhu Zhang3Ming Chen4Peng Yan5State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, ChinaState Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, ChinaChangjiang Institute of Survey, Planning, Design and Research, Wuhan 430010, ChinaChangjiang Institute of Survey, Planning, Design and Research, Wuhan 430010, ChinaState Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, ChinaState Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, ChinaThe 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.https://www.mdpi.com/1424-8220/21/7/2473blast-induced damagehigh rock slopesonic testblasting vibrationnatural frequencyBayesian linear regression |
| spellingShingle | Pengchang Sun Wenbo Lu Haoran Hu Yuzhu Zhang Ming Chen Peng Yan A Bayesian Approach to Predict Blast-Induced Damage of High Rock Slope Using Vibration and Sonic Data Sensors blast-induced damage high rock slope sonic test blasting vibration natural frequency Bayesian linear regression |
| 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 | blast-induced damage high rock slope sonic test blasting vibration natural frequency Bayesian linear regression |
| url | https://www.mdpi.com/1424-8220/21/7/2473 |
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