Prediction of seismic damage spectra using computational intelligence methods
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- @Article{GHAREHBAGHI:2021:CS,
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author = "Sadjad Gharehbaghi and Mostafa Gandomi and
Vagelis Plevris and Amir H. Gandomi",
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title = "Prediction of seismic damage spectra using
computational intelligence methods",
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journal = "Computer \& Structures",
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volume = "253",
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pages = "106584",
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year = "2021",
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ISSN = "0045-7949",
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DOI = "doi:10.1016/j.compstruc.2021.106584",
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URL = "https://www.sciencedirect.com/science/article/pii/S0045794921001061",
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keywords = "genetic algorithms, genetic programming, Computational
intelligence, Artificial neural networks, Regression
analysis, Seismic damage spectra, Inelastic SDOF
systems, Park-Ang damage index, Resiliency",
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abstract = "Predicting 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",
- }
Genetic Programming entries for
Sadjad Gharehbaghi
Mostafa Gandomi
Vagelis Plevris
A H Gandomi
Citations