Prediction for underground seismic intensity measures using conditional generative adversarial networks.
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| Title: | Prediction for underground seismic intensity measures using conditional generative adversarial networks. |
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| Authors: | Duan, Shuqian1 (AUTHOR), Song, Zebin2,3 (AUTHOR), Shen, Jiaxu1,4,5 (AUTHOR) shenjx@bjut.edu.cn, Xiong, Jiecheng1 (AUTHOR) |
| Source: | Soil Dynamics & Earthquake Engineering (0267-7261). May2024, Vol. 180, pN.PAG-N.PAG. 1p. |
| Subjects: | Generative adversarial networks, Distribution (Probability theory), Underground construction, Earthquake resistant design, Conditional probability, Probabilistic generative models, Simultaneous equations |
| Abstract: | With the escalating development and utilization of subterranean spaces, the seismic hazards faced by underground structures are progressively increasing. However, owing to the challenges associated with acquiring underground seismic data and historical seismic design norms, pertinent regulations and research in this domain are scarce. This study focused on three crucial intensity measures in the seismic design process of underground structures peak ground acceleration (PGA), peak ground velocity (PGV), and peak ground displacement (PGD). The research leverages seismic data obtained from the California Strong Motion Instrumentation Program (CSMIP) to train and evaluate a conditional generative adversarial network (CGAN) model. This model was employed to establish a multivariate joint conditional probability distribution among the intensity measures at varying depths, facilitating the stochastic prediction of shallow intensity measures. In contrast to empirical formulas, the CGAN model eliminates the need for a predefined equation structure and enables the simultaneous prediction of multiple intensity measures. The performance of the model was evaluated by comparing the predictive accuracy of the CGAN model and empirical fitting formulas across diverse site conditions and depth intervals using metrics such as relative error coefficients. It can be concluded that the proposed CGAN model can accurately predict shallow seismic intensity measures, and the predictions conform to a specific conditional distribution while retaining the stochastic nature of seismic motion. Compared with empirical formula models, the CGAN model exhibited an enhanced predictive capability. • CGAN model extracts inter-depth seismic intensity measures distribution. • Predictions of the CGAN model are more accurate than empirical formula model. • CGAN model outperforms for larger seismic intensity measures. • CGAN model exhibits varied performance with site and depth differences. [ABSTRACT FROM AUTHOR] |
| Copyright of Soil Dynamics & Earthquake Engineering (0267-7261) is the property of Elsevier B.V. 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 176588379 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Prediction for underground seismic intensity measures using conditional generative adversarial networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Duan%2C+Shuqian%22">Duan, Shuqian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Song%2C+Zebin%22">Song, Zebin</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shen%2C+Jiaxu%22">Shen, Jiaxu</searchLink><relatesTo>1,4,5</relatesTo> (AUTHOR)<i> shenjx@bjut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xiong%2C+Jiecheng%22">Xiong, Jiecheng</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Soil+Dynamics+%26+Earthquake+Engineering+%280267-7261%29%22">Soil Dynamics & Earthquake Engineering (0267-7261)</searchLink>. May2024, Vol. 180, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Generative+adversarial+networks%22">Generative adversarial networks</searchLink><br /><searchLink fieldCode="DE" term="%22Distribution+%28Probability+theory%29%22">Distribution (Probability theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Underground+construction%22">Underground construction</searchLink><br /><searchLink fieldCode="DE" term="%22Earthquake+resistant+design%22">Earthquake resistant design</searchLink><br /><searchLink fieldCode="DE" term="%22Conditional+probability%22">Conditional probability</searchLink><br /><searchLink fieldCode="DE" term="%22Probabilistic+generative+models%22">Probabilistic generative models</searchLink><br /><searchLink fieldCode="DE" term="%22Simultaneous+equations%22">Simultaneous equations</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: With the escalating development and utilization of subterranean spaces, the seismic hazards faced by underground structures are progressively increasing. However, owing to the challenges associated with acquiring underground seismic data and historical seismic design norms, pertinent regulations and research in this domain are scarce. This study focused on three crucial intensity measures in the seismic design process of underground structures peak ground acceleration (PGA), peak ground velocity (PGV), and peak ground displacement (PGD). The research leverages seismic data obtained from the California Strong Motion Instrumentation Program (CSMIP) to train and evaluate a conditional generative adversarial network (CGAN) model. This model was employed to establish a multivariate joint conditional probability distribution among the intensity measures at varying depths, facilitating the stochastic prediction of shallow intensity measures. In contrast to empirical formulas, the CGAN model eliminates the need for a predefined equation structure and enables the simultaneous prediction of multiple intensity measures. The performance of the model was evaluated by comparing the predictive accuracy of the CGAN model and empirical fitting formulas across diverse site conditions and depth intervals using metrics such as relative error coefficients. It can be concluded that the proposed CGAN model can accurately predict shallow seismic intensity measures, and the predictions conform to a specific conditional distribution while retaining the stochastic nature of seismic motion. Compared with empirical formula models, the CGAN model exhibited an enhanced predictive capability. • CGAN model extracts inter-depth seismic intensity measures distribution. • Predictions of the CGAN model are more accurate than empirical formula model. • CGAN model outperforms for larger seismic intensity measures. • CGAN model exhibits varied performance with site and depth differences. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Soil Dynamics & Earthquake Engineering (0267-7261) is the property of Elsevier B.V. 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: 10.1016/j.soildyn.2024.108619 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Generative adversarial networks Type: general – SubjectFull: Distribution (Probability theory) Type: general – SubjectFull: Underground construction Type: general – SubjectFull: Earthquake resistant design Type: general – SubjectFull: Conditional probability Type: general – SubjectFull: Probabilistic generative models Type: general – SubjectFull: Simultaneous equations Type: general Titles: – TitleFull: Prediction for underground seismic intensity measures using conditional generative adversarial networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Duan, Shuqian – PersonEntity: Name: NameFull: Song, Zebin – PersonEntity: Name: NameFull: Shen, Jiaxu – PersonEntity: Name: NameFull: Xiong, Jiecheng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 02677261 Numbering: – Type: volume Value: 180 Titles: – TitleFull: Soil Dynamics & Earthquake Engineering (0267-7261) Type: main |
| ResultId | 1 |
