Nonlinear Intelligent Inversion Method and Practice for In-situ Stress in Stratified Rock Masses with Deep Valley.
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| Title: | Nonlinear Intelligent Inversion Method and Practice for In-situ Stress in Stratified Rock Masses with Deep Valley. |
|---|---|
| Authors: | Song, Zebin1,2 (AUTHOR), Jiang, Quan1 (AUTHOR) qjiang@whrsm.ac.cn, Chen, Pengfei1,2 (AUTHOR), Xia, Yong3 (AUTHOR), Xiang, Tianbing4 (AUTHOR) |
| Source: | Rock Mechanics & Rock Engineering. Feb2025, Vol. 58 Issue 2, p1933-1955. 23p. |
| Subjects: | Long short-term memory, Valleys, Deep learning, Caves, Methods engineering |
| Abstract: | Accurate initial stress conditions, including their magnitude and orientation, are a prerequisite for evaluating the deformation and failure of surrounding rock masses in underground caverns, as well as for assessing dynamic failure risks and optimizing the layout of the cavern's axes. Yet, the general stress inversion method based on limited measured data points and numerical back analysis always faces the challenge of non-uniqueness in the model's boundary conditions, which results in inaccuracies of the obtained stress field. This study established an intelligent inversion method for the stress field in stratified rock masses with deep valleys, considering the effects of river erosion. It proposed a nonlinear inversion algorithm based on Long Short-Term Memory Networks-Hybrid Optimization (LSTM-HO), which leverages the structural advantages of LSTM to construct a precise nonlinear surrogate model between measured geo-stress and the model's boundary conditions, and by utilizing the fitness function of HO to search the reasonable boundary conditions. By inputting measured geo-stress, the HO can rapidly generate several potential boundary condition vectors of the numerical model. The cavern's excavation damage zone (EDZ) is then employed for secondary screening, refining these possibilities to a unique, reliable boundary vector. This approach effectively addresses the non-uniqueness issue of the model's boundary conditions in stress field inversion processes. The method is applied in the engineering site of Kala Hydropower Station. A reliable regional stress field consistent with measured stresses and the cavern's EDZ depth is obtained in steeply inclined layered rock masses with V-shaped river valleys, which demonstrates the reliability and applicability of the presented method. Highlights: Developed a numerical model for in-situ stress in steeply inclined layered rock masses, considering river erosion and major faults. An intelligent nonlinear inversion framework using Long Short-Term Memory-Hybrid Optimization was proposed to create potential in-situ stress fields. The excavation damage zone was included as an inversion indicator to address non-uniqueness in boundary conditions. The causes and effects of non-uniqueness in boundary conditions on in-situ stress inversion were analyzed. [ABSTRACT FROM AUTHOR] |
| Copyright of Rock Mechanics & Rock Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 183282744 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Nonlinear Intelligent Inversion Method and Practice for In-situ Stress in Stratified Rock Masses with Deep Valley. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Song%2C+Zebin%22">Song, Zebin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Quan%22">Jiang, Quan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> qjiang@whrsm.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Pengfei%22">Chen, Pengfei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xia%2C+Yong%22">Xia, Yong</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xiang%2C+Tianbing%22">Xiang, Tianbing</searchLink><relatesTo>4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Rock+Mechanics+%26+Rock+Engineering%22">Rock Mechanics & Rock Engineering</searchLink>. Feb2025, Vol. 58 Issue 2, p1933-1955. 23p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Valleys%22">Valleys</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Caves%22">Caves</searchLink><br /><searchLink fieldCode="DE" term="%22Methods+engineering%22">Methods engineering</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Accurate initial stress conditions, including their magnitude and orientation, are a prerequisite for evaluating the deformation and failure of surrounding rock masses in underground caverns, as well as for assessing dynamic failure risks and optimizing the layout of the cavern's axes. Yet, the general stress inversion method based on limited measured data points and numerical back analysis always faces the challenge of non-uniqueness in the model's boundary conditions, which results in inaccuracies of the obtained stress field. This study established an intelligent inversion method for the stress field in stratified rock masses with deep valleys, considering the effects of river erosion. It proposed a nonlinear inversion algorithm based on Long Short-Term Memory Networks-Hybrid Optimization (LSTM-HO), which leverages the structural advantages of LSTM to construct a precise nonlinear surrogate model between measured geo-stress and the model's boundary conditions, and by utilizing the fitness function of HO to search the reasonable boundary conditions. By inputting measured geo-stress, the HO can rapidly generate several potential boundary condition vectors of the numerical model. The cavern's excavation damage zone (EDZ) is then employed for secondary screening, refining these possibilities to a unique, reliable boundary vector. This approach effectively addresses the non-uniqueness issue of the model's boundary conditions in stress field inversion processes. The method is applied in the engineering site of Kala Hydropower Station. A reliable regional stress field consistent with measured stresses and the cavern's EDZ depth is obtained in steeply inclined layered rock masses with V-shaped river valleys, which demonstrates the reliability and applicability of the presented method. Highlights: Developed a numerical model for in-situ stress in steeply inclined layered rock masses, considering river erosion and major faults. An intelligent nonlinear inversion framework using Long Short-Term Memory-Hybrid Optimization was proposed to create potential in-situ stress fields. The excavation damage zone was included as an inversion indicator to address non-uniqueness in boundary conditions. The causes and effects of non-uniqueness in boundary conditions on in-situ stress inversion were analyzed. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Rock Mechanics & Rock Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00603-024-04233-6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 1933 Subjects: – SubjectFull: Long short-term memory Type: general – SubjectFull: Valleys Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Caves Type: general – SubjectFull: Methods engineering Type: general Titles: – TitleFull: Nonlinear Intelligent Inversion Method and Practice for In-situ Stress in Stratified Rock Masses with Deep Valley. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Song, Zebin – PersonEntity: Name: NameFull: Jiang, Quan – PersonEntity: Name: NameFull: Chen, Pengfei – PersonEntity: Name: NameFull: Xia, Yong – PersonEntity: Name: NameFull: Xiang, Tianbing IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 07232632 Numbering: – Type: volume Value: 58 – Type: issue Value: 2 Titles: – TitleFull: Rock Mechanics & Rock Engineering Type: main |
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