Empirical formulas and Artificial Neural Networks to estimate the fundamental periods of existing and instrumented RC buildings in Thailand

dc.contributor.authorOrnthammarath, Teraphan
dc.contributor.authorTha Toe, Tun Tun
dc.contributor.authorRupakhety, Rajesh
dc.contributor.authorMalaga-Chuquitaype, Christian
dc.contributor.authorRanaweera, Janaka
dc.contributor.authorPradittan, Prem
dc.contributor.departmentFaculty of Civil and Environmental Engineering
dc.date.accessioned2025-11-20T09:47:50Z
dc.date.available2025-11-20T09:47:50Z
dc.date.issued2025-04-15
dc.descriptionPublisher Copyright: © 2025 The Authorsen
dc.description.abstractEven though Bangkok is situated away from known active faults (about 150 km), due to the soft alluvial basin in and around the capital city of Thailand, several of these high-rise buildings experienced noticeable structural and non-structural responses caused by recent long-distance and moderate earthquakes from Mw > 7.5 in the Sumatra subduction zone and Mw > 6 in Myanmar, Northern Thailand, and Laos. This raises the awareness to assess the dynamic characteristics of buildings in Bangkok and other provinces in Thailand. In the current study, ambient vibration measurements have been performed on 98 reinforced concrete (RC) buildings to determine relationships between the building fundamental period and height of existing structures built prior and after seismic design code issued in 2009. The measured buildings’ height ranges of 7–142 m (2–35 stories) e.g., hospitals, condominiums, offices, etc. Different techniques are adopted to determine the two main translational fundamental periods in orthogonal directions of considered structures including Horizontal-to-vertical spectral ratio, Fourier spectrum analysis, and Half-power bandwidth method, and all considered methods show comparable results, and the empirical formulas are proposed. In order to validate this finding, the estimated fundamental periods of two instrumented hospitals from local and regional earthquakes give similar results to the proposed empirical formulas. In addition, artificial neural networks (ANNs) have been adopted to train and predict the fundamental period using the newly compiled database. For the same RC structures, the soil-structure interaction in Bangkok leading to a longer fundamental period than those reported in published literatures.en
dc.description.versionPeer revieweden
dc.format.extent2962005
dc.format.extent
dc.identifier.citationOrnthammarath, T, Tha Toe, T T, Rupakhety, R, Malaga-Chuquitaype, C, Ranaweera, J & Pradittan, P 2025, 'Empirical formulas and Artificial Neural Networks to estimate the fundamental periods of existing and instrumented RC buildings in Thailand', Journal of Building Engineering, vol. 100, 111691. https://doi.org/10.1016/j.jobe.2024.111691en
dc.identifier.doi10.1016/j.jobe.2024.111691
dc.identifier.issn2352-7102
dc.identifier.other235872629
dc.identifier.other10dc4a97-e781-4c2c-a4e9-c1b9d4db6dbc
dc.identifier.other85214287412
dc.identifier.urihttps://hdl.handle.net/20.500.11815/7749
dc.language.isoen
dc.relation.ispartofseriesJournal of Building Engineering; 100()en
dc.relation.urlhttps://www.scopus.com/pages/publications/85214287412en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.subjectAmbient vibrationen
dc.subjectArtificial neural networksen
dc.subjectEarthquakeen
dc.subjectFundamental period of buildingen
dc.subjectHVSRen
dc.subjectWorden
dc.subjectCivil and Structural Engineeringen
dc.subjectArchitectureen
dc.subjectBuilding and Constructionen
dc.subjectSafety, Risk, Reliability and Qualityen
dc.subjectMechanics of Materialsen
dc.titleEmpirical formulas and Artificial Neural Networks to estimate the fundamental periods of existing and instrumented RC buildings in Thailanden
dc.type/dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/articleen

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