Acquisition of acoustic emission precursor information for rock masses with a single joint based on clustering-convolutional neural network method
The method for precursor information acquisition based on acoustic emission (AE) data for jointed rock masses is of significant importance for the early warning of dynamic disasters in underground engineering. A clustering-convolutional neural network (CNN) method is proposed, which comprises a clus...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Journal of Rock Mechanics and Geotechnical Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674775524001781 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846128166453641216 |
|---|---|
| author | Peiyao Xie Weizhong Chen Wusheng Zhao Hou Gao |
| author_facet | Peiyao Xie Weizhong Chen Wusheng Zhao Hou Gao |
| author_sort | Peiyao Xie |
| collection | DOAJ |
| description | The method for precursor information acquisition based on acoustic emission (AE) data for jointed rock masses is of significant importance for the early warning of dynamic disasters in underground engineering. A clustering-convolutional neural network (CNN) method is proposed, which comprises a clustering component and a CNN component. A series of uniaxial compression tests were conducted on granite specimens containing a persistent sawtooth joint, with different strain rates (10−5–10−2 s−1) and joint inclination angles (0°–50°). The results demonstrate that traditional precursory indicators based on full waveforms are effective for obtaining precursor information of the intact rock failure. However, these indicators are not universally applicable to the failure of rock masses with a single joint. The clustering-CNN method has the potential to be applied to obtain precursor information for all three failure modes (Modes I, II and III). Following the waveform clustering analysis, the effective waveforms exhibit a low main frequency, as well as high energy, ringing count, and rise time. Furthermore, the clustering method and the precursory indicators influence the acquisition of final precursor information. The Birch hierarchical clustering method and the S value precursory indicator can help to obtain more accurate results. The findings of this study may contribute to the development of warning methods for underground engineering across faults. |
| format | Article |
| id | doaj-art-3dd9676e89ce4cbe97b749506472bbdd |
| institution | Kabale University |
| issn | 1674-7755 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Rock Mechanics and Geotechnical Engineering |
| spelling | doaj-art-3dd9676e89ce4cbe97b749506472bbdd2024-12-11T05:55:56ZengElsevierJournal of Rock Mechanics and Geotechnical Engineering1674-77552024-12-01161250615076Acquisition of acoustic emission precursor information for rock masses with a single joint based on clustering-convolutional neural network methodPeiyao Xie0Weizhong Chen1Wusheng Zhao2Hou Gao3State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, 430071, China; University of Chinese Academy of Sciences, Beijing, 100049, ChinaState Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, 430071, ChinaState Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, 430071, China; Corresponding author.State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, 430071, China; University of Chinese Academy of Sciences, Beijing, 100049, ChinaThe method for precursor information acquisition based on acoustic emission (AE) data for jointed rock masses is of significant importance for the early warning of dynamic disasters in underground engineering. A clustering-convolutional neural network (CNN) method is proposed, which comprises a clustering component and a CNN component. A series of uniaxial compression tests were conducted on granite specimens containing a persistent sawtooth joint, with different strain rates (10−5–10−2 s−1) and joint inclination angles (0°–50°). The results demonstrate that traditional precursory indicators based on full waveforms are effective for obtaining precursor information of the intact rock failure. However, these indicators are not universally applicable to the failure of rock masses with a single joint. The clustering-CNN method has the potential to be applied to obtain precursor information for all three failure modes (Modes I, II and III). Following the waveform clustering analysis, the effective waveforms exhibit a low main frequency, as well as high energy, ringing count, and rise time. Furthermore, the clustering method and the precursory indicators influence the acquisition of final precursor information. The Birch hierarchical clustering method and the S value precursory indicator can help to obtain more accurate results. The findings of this study may contribute to the development of warning methods for underground engineering across faults.http://www.sciencedirect.com/science/article/pii/S1674775524001781Acoustic Emission (AE)Precursor information acquisitionPrecursory indicatorClustering-Convolutional Neural Network (CNN) methodRock mass failureSingle joint |
| spellingShingle | Peiyao Xie Weizhong Chen Wusheng Zhao Hou Gao Acquisition of acoustic emission precursor information for rock masses with a single joint based on clustering-convolutional neural network method Journal of Rock Mechanics and Geotechnical Engineering Acoustic Emission (AE) Precursor information acquisition Precursory indicator Clustering-Convolutional Neural Network (CNN) method Rock mass failure Single joint |
| title | Acquisition of acoustic emission precursor information for rock masses with a single joint based on clustering-convolutional neural network method |
| title_full | Acquisition of acoustic emission precursor information for rock masses with a single joint based on clustering-convolutional neural network method |
| title_fullStr | Acquisition of acoustic emission precursor information for rock masses with a single joint based on clustering-convolutional neural network method |
| title_full_unstemmed | Acquisition of acoustic emission precursor information for rock masses with a single joint based on clustering-convolutional neural network method |
| title_short | Acquisition of acoustic emission precursor information for rock masses with a single joint based on clustering-convolutional neural network method |
| title_sort | acquisition of acoustic emission precursor information for rock masses with a single joint based on clustering convolutional neural network method |
| topic | Acoustic Emission (AE) Precursor information acquisition Precursory indicator Clustering-Convolutional Neural Network (CNN) method Rock mass failure Single joint |
| url | http://www.sciencedirect.com/science/article/pii/S1674775524001781 |
| work_keys_str_mv | AT peiyaoxie acquisitionofacousticemissionprecursorinformationforrockmasseswithasinglejointbasedonclusteringconvolutionalneuralnetworkmethod AT weizhongchen acquisitionofacousticemissionprecursorinformationforrockmasseswithasinglejointbasedonclusteringconvolutionalneuralnetworkmethod AT wushengzhao acquisitionofacousticemissionprecursorinformationforrockmasseswithasinglejointbasedonclusteringconvolutionalneuralnetworkmethod AT hougao acquisitionofacousticemissionprecursorinformationforrockmasseswithasinglejointbasedonclusteringconvolutionalneuralnetworkmethod |