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AuthorGharehbaghi, Sadjad
AuthorGandomi, Mostafa
AuthorPlevris, Vagelis
AuthorGandomi, Amir H.
Available date2023-12-06T09:34:56Z
Publication Date2021
Publication NameComputers and Structures
ResourceScopus
ISSN457949
URIhttp://dx.doi.org/10.1016/j.compstruc.2021.106584
URIhttp://hdl.handle.net/10576/50173
AbstractPredicting seismic damage spectra, capturing both structural and earthquake features, is useful in performance-based seismic design and quantifying the potential seismic damage of structures. The objective of this paper is to accurately predict the seismic damage spectra using computational intelligence methods. For this purpose, an inelastic single-degree-of-freedom system subjected to a set of earthquake ground motion records is used to compute the (exact) spectral damage. The Park-Ang damage index is used to quantify the seismic damage. Both structural and earthquake features are involved in the prediction models where multi-gene genetic programming (MGGP) and artificial neural networks (ANNs) are applied. Common performance metrics were used to assess the models developed for seismic damage spectra, and indicated that their accuracy was higher than a corresponding model in the literature. Although the performance metrics revealed that the ANN model is more accurate than the MGGP model, the explicit MGGP-based mathematical model renders it more practical in quantifying the potential seismic damage of structures.
Languageen
PublisherElsevier
SubjectArtificial neural networks
Computational intelligence
Genetic programming
Inelastic SDOF systems
Park-Ang damage index
Regression analysis
Resiliency
Seismic damage spectra
TitlePrediction of seismic damage spectra using computational intelligence methods
TypeArticle
Volume Number253
dc.accessType Abstract Only


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