Study on critical slowdown characteristics and early warning model of damage evolution of sandstone under freeze–thaw cycles
Freeze–thaw damage of rock mass poses a great threat to the safety of rock engineering, ground buildings, and low-temperature storage of liquefied natural gas (LNG) in cold regions. By collecting acoustic emission (AE) signals of sandstone during uniaxial compression failures, this paper analyzed th...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2022.1006642/full |
| _version_ | 1797956684884213760 |
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| author | Jiaxu Jin Jiaxu Jin Xinlei Zhang Xiaoli Liu Yahao Li Shaohua Li |
| author_facet | Jiaxu Jin Jiaxu Jin Xinlei Zhang Xiaoli Liu Yahao Li Shaohua Li |
| author_sort | Jiaxu Jin |
| collection | DOAJ |
| description | Freeze–thaw damage of rock mass poses a great threat to the safety of rock engineering, ground buildings, and low-temperature storage of liquefied natural gas (LNG) in cold regions. By collecting acoustic emission (AE) signals of sandstone during uniaxial compression failures, this paper analyzed the critical slowdown phenomenon of different types of sandstone during the freeze–thaw failure. According to the auto-correlation coefficients and the variance of AE signals under different windows and steps, the precursors were determined and a warning model of rock engineering failure precursors based on the critical slowdown principle was proposed. Then the Grey Wolf Optimizer (GWO) algorithm was used to optimize the initial weights and thresholds of the back propagation (BP) neural network, and the influence factors of rock engineering failure under different working conditions were input as training sets to train the network. The results showed that the correlation coefficients between the predicted value and real value of the GWO-BP neural network reached 99.90% and 98.81% respectively, indicating that the accuracy of the BP neural network prediction was improved. This study provides a new method for rock engineering failure early warning, and has great theoretical and guiding significance for enriching and improving the rock mass AE monitoring technology. |
| first_indexed | 2024-04-10T23:52:46Z |
| format | Article |
| id | doaj.art-b30dc325c86a4cad8e878194f3fc2b34 |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-04-10T23:52:46Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-b30dc325c86a4cad8e878194f3fc2b342023-01-10T16:11:05ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632023-01-011010.3389/feart.2022.10066421006642Study on critical slowdown characteristics and early warning model of damage evolution of sandstone under freeze–thaw cyclesJiaxu Jin0Jiaxu Jin1Xinlei Zhang2Xiaoli Liu3Yahao Li4Shaohua Li5School of Civil Engineering, Liaoning Technical University, Fuxin, ChinaLiaoning Key Laboratory of Mine Subsidence Disaster Prevention and Control, Fuxin, ChinaSchool of Civil Engineering, Liaoning Technical University, Fuxin, ChinaState Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, ChinaSchool of Civil Engineering, Liaoning Technical University, Fuxin, ChinaDepartment of the Built Environment, Eindhoven University of Technology, Eindhoven, NetherlandsFreeze–thaw damage of rock mass poses a great threat to the safety of rock engineering, ground buildings, and low-temperature storage of liquefied natural gas (LNG) in cold regions. By collecting acoustic emission (AE) signals of sandstone during uniaxial compression failures, this paper analyzed the critical slowdown phenomenon of different types of sandstone during the freeze–thaw failure. According to the auto-correlation coefficients and the variance of AE signals under different windows and steps, the precursors were determined and a warning model of rock engineering failure precursors based on the critical slowdown principle was proposed. Then the Grey Wolf Optimizer (GWO) algorithm was used to optimize the initial weights and thresholds of the back propagation (BP) neural network, and the influence factors of rock engineering failure under different working conditions were input as training sets to train the network. The results showed that the correlation coefficients between the predicted value and real value of the GWO-BP neural network reached 99.90% and 98.81% respectively, indicating that the accuracy of the BP neural network prediction was improved. This study provides a new method for rock engineering failure early warning, and has great theoretical and guiding significance for enriching and improving the rock mass AE monitoring technology.https://www.frontiersin.org/articles/10.3389/feart.2022.1006642/fullfreeze-thaw cycleacoustic emissioncritical slowdownprecursorsGWO-BP neural network |
| spellingShingle | Jiaxu Jin Jiaxu Jin Xinlei Zhang Xiaoli Liu Yahao Li Shaohua Li Study on critical slowdown characteristics and early warning model of damage evolution of sandstone under freeze–thaw cycles Frontiers in Earth Science freeze-thaw cycle acoustic emission critical slowdown precursors GWO-BP neural network |
| title | Study on critical slowdown characteristics and early warning model of damage evolution of sandstone under freeze–thaw cycles |
| title_full | Study on critical slowdown characteristics and early warning model of damage evolution of sandstone under freeze–thaw cycles |
| title_fullStr | Study on critical slowdown characteristics and early warning model of damage evolution of sandstone under freeze–thaw cycles |
| title_full_unstemmed | Study on critical slowdown characteristics and early warning model of damage evolution of sandstone under freeze–thaw cycles |
| title_short | Study on critical slowdown characteristics and early warning model of damage evolution of sandstone under freeze–thaw cycles |
| title_sort | study on critical slowdown characteristics and early warning model of damage evolution of sandstone under freeze thaw cycles |
| topic | freeze-thaw cycle acoustic emission critical slowdown precursors GWO-BP neural network |
| url | https://www.frontiersin.org/articles/10.3389/feart.2022.1006642/full |
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