ANN-Based Ground Motion and Physics-Based Broadband Models for Vertical Spectra: ANN-Based Ground Motion...: V. Sharma et al.
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| Title: | ANN-Based Ground Motion and Physics-Based Broadband Models for Vertical Spectra: ANN-Based Ground Motion...: V. Sharma et al. |
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| Authors: | Sharma, Varun1 (AUTHOR) d21029@students.iitmandi.ac.in, Author, Harsh Kumar Arya1 (AUTHOR) t22453@students.iitmandi.ac.in, Gade, Maheshreddy1 (AUTHOR) maheshreddy@iitmandi.ac.in, Dhanya, J.1 (AUTHOR) dhanya@iitmandi.ac.in |
| Source: | Pure & Applied Geophysics. Feb2025, Vol. 182 Issue 2, p637-665. 29p. |
| Subjects: | Artificial neural networks, Ground motion, Vertical motion, Artificial intelligence, Image processing |
| Abstract: | This study proposes a new simplified Ground Motion Model (GMM) for vertical spectra by combining comprehensive datasets from the NESS and NGA-West2 databases. The proposed Artificial Neural Network (ANN) architecture-based model requires only 288 unknowns to predict spectral accelerations (Sa) at 33 distinct periods ranging from 0 to 4 s. Notably, this model inherently captures known physical phenomena with reduced variability using a minimum number of unknowns compared to the GMMs existing literature, thus offering a valuable addition to current hazard estimation frameworks. Furthermore, recognizing the necessity for physics-based simulations in vertical ground motion analysis, we introduce a physics-based broadband model for vertical spectra using ANN methodology. The proposed broadband model exhibits better robustness due to the comprehensiveness of the dataset utilized and the inclusion of source path and site characteristics at the input layer. Additionally, the model effectively captures the physical trends with minimal deviation. Further, we verified the predictive ability of the developed models through a comprehensive case study of the 2008 Iwate–Miyagi earthquake. The proposed models serve as essential tools for physics-based broadband simulations and hazard assessments in active shallow crustal regions. [ABSTRACT FROM AUTHOR] |
| Copyright of Pure & Applied Geophysics 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 183752586 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: ANN-Based Ground Motion and Physics-Based Broadband Models for Vertical Spectra: ANN-Based Ground Motion...: V. Sharma et al. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sharma%2C+Varun%22">Sharma, Varun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> d21029@students.iitmandi.ac.in</i><br /><searchLink fieldCode="AR" term="%22Author%2C+Harsh+Kumar+Arya%22">Author, Harsh Kumar Arya</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> t22453@students.iitmandi.ac.in</i><br /><searchLink fieldCode="AR" term="%22Gade%2C+Maheshreddy%22">Gade, Maheshreddy</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> maheshreddy@iitmandi.ac.in</i><br /><searchLink fieldCode="AR" term="%22Dhanya%2C+J%2E%22">Dhanya, J.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> dhanya@iitmandi.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Pure+%26+Applied+Geophysics%22">Pure & Applied Geophysics</searchLink>. Feb2025, Vol. 182 Issue 2, p637-665. 29p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Ground+motion%22">Ground motion</searchLink><br /><searchLink fieldCode="DE" term="%22Vertical+motion%22">Vertical motion</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study proposes a new simplified Ground Motion Model (GMM) for vertical spectra by combining comprehensive datasets from the NESS and NGA-West2 databases. The proposed Artificial Neural Network (ANN) architecture-based model requires only 288 unknowns to predict spectral accelerations (Sa) at 33 distinct periods ranging from 0 to 4 s. Notably, this model inherently captures known physical phenomena with reduced variability using a minimum number of unknowns compared to the GMMs existing literature, thus offering a valuable addition to current hazard estimation frameworks. Furthermore, recognizing the necessity for physics-based simulations in vertical ground motion analysis, we introduce a physics-based broadband model for vertical spectra using ANN methodology. The proposed broadband model exhibits better robustness due to the comprehensiveness of the dataset utilized and the inclusion of source path and site characteristics at the input layer. Additionally, the model effectively captures the physical trends with minimal deviation. Further, we verified the predictive ability of the developed models through a comprehensive case study of the 2008 Iwate–Miyagi earthquake. The proposed models serve as essential tools for physics-based broadband simulations and hazard assessments in active shallow crustal regions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Pure & Applied Geophysics 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/s00024-025-03660-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 637 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Ground motion Type: general – SubjectFull: Vertical motion Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Image processing Type: general Titles: – TitleFull: ANN-Based Ground Motion and Physics-Based Broadband Models for Vertical Spectra: ANN-Based Ground Motion...: V. Sharma et al. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sharma, Varun – PersonEntity: Name: NameFull: Author, Harsh Kumar Arya – PersonEntity: Name: NameFull: Gade, Maheshreddy – PersonEntity: Name: NameFull: Dhanya, J. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00334553 Numbering: – Type: volume Value: 182 – Type: issue Value: 2 Titles: – TitleFull: Pure & Applied Geophysics Type: main |
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