Prediction of seismic damage spectra using computational intelligence methods. (September 2021)

• Record Type: Journal Article
• Authors: Gharehbaghi, Sadjad; Gandomi, Mostafa; Plevris, Vagelis; Gandomi, Amir H.
• Is Part Of: Computers & structures. Volume 253(2021)
• Publication Date: 2021-09
• Subjects: Computational intelligence -- Genetic programming -- Artificial neural networks -- Regression analysis -- Seismic damage spectra -- Inelastic SDOF systems -- Park-Ang damage index -- Resiliency; Structural engineering -- Data processing -- Periodicals; Electronic data processing -- Structures, Theory of -- Periodicals; 624.171
• DOI: 10.1016/j.compstruc.2021.106584
• Languages: English
• ISSNs: 0045-7949

Abstract

Highlights: An Artificial Neural Network model is developed for seismic spectral damage. A multi-objective Genetic Programming is implemented to formulate the problem. Both structural and earthquake features have been included in the proposed models. Six performance metrics have been employed for validation and comparison purposes. Benchmarking with an existing method shows the superiority of the proposed models. 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.