| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-12-18 |
| タイトル |
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|
タイトル |
A real-time evaluation method of unstable rock risk level based on Microseismic data and CWT-CNN integrated algorithm |
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言語 |
en |
| 言語 |
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|
言語 |
eng |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
unstable rock |
| キーワード |
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言語 |
en |
|
主題Scheme |
Other |
|
主題 |
continuous wavelet transform |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
convolutional neural networks |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
risk assessment |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| 著者 |
Du, Yan
Li, Renjian
Xie, Mowen
Jiang, Yujing
CHICAS, Santos Daniel
Liu, Jingnan
Lu, Weikang
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
Geological hazards caused by unstable rocks, including rock collapse and fall, pose significant threats to both engineering productivity and the safety of residents, resulting in substantial economic losses. This study proposed a comprehensive CNN (Convolutional Neural Networks) classification recognition algorithm based on continuous wavelet analysis of microseismic monitoring data, which establishes a CNN recognition-based method for identifying risk levels of unstable rocks to predict their real-time collapse. To collect training data, fixed-base tests were conducted, and validation was performed using vibration data from freeze-thaw tests. Results of the study showed that the accuracy of the CNN classification recognition model trained with fixed-base test data reached 97.6%, and the per-second classification accuracy of vibration segments from freeze-thaw tests was above 86%. Furthermore, the study discussed the correlation between the calculated risk-level eigenvalues and safety coefficients. The CNN risk-level eigenvalues were found to be highly negatively correlated with the safety coefficients, with correlation coefficients as high as 0.8703. Finally, the study verified the superiority of the precursor identification of the risk-level evaluation method by comparing it with the safety coefficients. |
|
言語 |
en |
| 書誌情報 |
en : AIMS Environmental Science
巻 12,
号 6,
p. 979-998,
発行日 2025-11-28
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| 出版者 |
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出版者 |
American Institute of Mathematical Sciences |
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言語 |
en |
| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
2372-0352 |
| DOI |
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関連タイプ |
isIdenticalTo |
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|
識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.3934/environsci.2025043 |
| 権利 |
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|
権利情報 |
© 2025 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0) |
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言語 |
en |
| 著者版フラグ |
|
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出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| 引用 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
AIMS Environmental Science, 12(6), pp.979-998; 2025 |
|
言語 |
en |
AIMSES12_979.pdf (1.2 MB)
