Academic Journal
Automated data processing and analysis method for shaking table tests of masonry structures based on python open-source toolchain.
| Τίτλος: | Automated data processing and analysis method for shaking table tests of masonry structures based on python open-source toolchain. |
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| Συγγραφείς: | yao, Zheng1 (AUTHOR), Yijun, Wang1 (AUTHOR) 2508826897@qq.com |
| Πηγή: | Science Progress. Apr-Jun2026, Vol. 109 Issue 2, p1-15. 15p. |
| Θεματικοί όροι: | *Shaking table tests, *Electronic data processing, *Power spectra, *Software frameworks, *Masonry, *Structural health monitoring, *Time-frequency analysis, *Seismic response |
| Περίληψη: | Shaking table testing is a crucial method for evaluating the seismic performance of structures; however, the resulting data are typically characterized by massive volumes, high sampling rates, and complex multi-channel arrays. Traditional manual processing methods relying on commercial spreadsheet software (e.g., Excel, Origin) present significant limitations regarding processing efficiency, mathematical transparency, and result reproducibility. To address these methodological gaps, this paper proposes a novel, fully automated data processing and analysis framework tailored for high-density structural dynamic testing using an open-source Python toolchain. Unlike conventional "black-box' commercial software, this method provides a transparent, end-to-end pipeline—from automated raw multi-channel data alignment and signal pre-processing to advanced time-frequency domain analysis and standardized visualization. The framework's efficacy is validated using a shaking table test of a 1:2 scaled village masonry structure. The extracted experimental results clearly indicate that the masonry structure exhibits a significant low-pass filtering effect on high-frequency inputs (5–15 Hz), with response energy concentrated within the natural frequency range of 2–4 Hz. Furthermore, the pipeline integrates an automated structural health evaluation module; by comparing the Power Spectral Density (PSD) of white noise sweeps before and after seismic inputs, the method successfully and rapidly identified that while the structure exhibited displacement amplification under the 0.2 g operating condition, no significant stiffness degradation occurred. Ultimately, this study contributes a scalable, reproducible, and highly efficient methodological blueprint for big data analysis in structural seismic evaluation. [ABSTRACT FROM AUTHOR] |
| Βάση Δεδομένων: | Academic Search Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:asx&genre=article&issn=00368504&ISBN=&volume=109&issue=2&date=20260401&spage=1&pages=1-15&title=Science Progress&atitle=Automated%20data%20processing%20and%20analysis%20method%20for%20shaking%20table%20tests%20of%20masonry%20structures%20based%20on%20python%20open-source%20toolchain.&aulast=yao%2C%20Zheng&id=DOI:10.1177/00368504261465266 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Header | DbId: asx DbLabel: Academic Search Index An: 194993413 RelevancyScore: 1431 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1430.75964355469 |
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| Items | – Name: Title Label: Title Group: Ti Data: Automated data processing and analysis method for shaking table tests of masonry structures based on python open-source toolchain. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22yao%2C+Zheng%22">yao, Zheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yijun%2C+Wang%22">Yijun, Wang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 2508826897@qq.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Science+Progress%22">Science Progress</searchLink>. Apr-Jun2026, Vol. 109 Issue 2, p1-15. 15p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Shaking+table+tests%22">Shaking table tests</searchLink><br />*<searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br />*<searchLink fieldCode="DE" term="%22Power+spectra%22">Power spectra</searchLink><br />*<searchLink fieldCode="DE" term="%22Software+frameworks%22">Software frameworks</searchLink><br />*<searchLink fieldCode="DE" term="%22Masonry%22">Masonry</searchLink><br />*<searchLink fieldCode="DE" term="%22Structural+health+monitoring%22">Structural health monitoring</searchLink><br />*<searchLink fieldCode="DE" term="%22Time-frequency+analysis%22">Time-frequency analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Seismic+response%22">Seismic response</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Shaking table testing is a crucial method for evaluating the seismic performance of structures; however, the resulting data are typically characterized by massive volumes, high sampling rates, and complex multi-channel arrays. Traditional manual processing methods relying on commercial spreadsheet software (e.g., Excel, Origin) present significant limitations regarding processing efficiency, mathematical transparency, and result reproducibility. To address these methodological gaps, this paper proposes a novel, fully automated data processing and analysis framework tailored for high-density structural dynamic testing using an open-source Python toolchain. Unlike conventional "black-box' commercial software, this method provides a transparent, end-to-end pipeline—from automated raw multi-channel data alignment and signal pre-processing to advanced time-frequency domain analysis and standardized visualization. The framework's efficacy is validated using a shaking table test of a 1:2 scaled village masonry structure. The extracted experimental results clearly indicate that the masonry structure exhibits a significant low-pass filtering effect on high-frequency inputs (5–15 Hz), with response energy concentrated within the natural frequency range of 2–4 Hz. Furthermore, the pipeline integrates an automated structural health evaluation module; by comparing the Power Spectral Density (PSD) of white noise sweeps before and after seismic inputs, the method successfully and rapidly identified that while the structure exhibited displacement amplification under the 0.2 g operating condition, no significant stiffness degradation occurred. Ultimately, this study contributes a scalable, reproducible, and highly efficient methodological blueprint for big data analysis in structural seismic evaluation. [ABSTRACT FROM AUTHOR] |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/00368504261465266 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1 Subjects: – SubjectFull: Shaking table tests Type: general – SubjectFull: Electronic data processing Type: general – SubjectFull: Power spectra Type: general – SubjectFull: Software frameworks Type: general – SubjectFull: Masonry Type: general – SubjectFull: Structural health monitoring Type: general – SubjectFull: Time-frequency analysis Type: general – SubjectFull: Seismic response Type: general Titles: – TitleFull: Automated data processing and analysis method for shaking table tests of masonry structures based on python open-source toolchain. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: yao, Zheng – PersonEntity: Name: NameFull: Yijun, Wang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr-Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00368504 Numbering: – Type: volume Value: 109 – Type: issue Value: 2 Titles: – TitleFull: Science Progress Type: main |
| ResultId | 1 |