Empirical formulas and Artificial Neural Networks to estimate the fundamental periods of existing and instrumented RC buildings in Thailand
Issued Date
2025-04-15
Resource Type
eISSN
23527102
Scopus ID
2-s2.0-85214287412
Journal Title
Journal of Building Engineering
Volume
100
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Building Engineering Vol.100 (2025)
Suggested Citation
Ornthammarath T., Tha Toe T.T., Rupakhety R., Malaga-Chuquitaype C., Ranaweera J., Pradittan P. 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 (2025). doi:10.1016/j.jobe.2024.111691 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/102752
Title
Empirical formulas and Artificial Neural Networks to estimate the fundamental periods of existing and instrumented RC buildings in Thailand
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Abstract
Even 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.