Frequency-based Data-driven Surrogate Model for Efficient Prediction of Irregular Structure's Seismic Responses.

Autor:	Dang-Vu, Hoang, Nguyen, Quang Dang, Chung, TaeChoong, Shin, Jiuk, Lee, Kihak
Předmět:	SEISMIC response ARTIFICIAL neural networks PREDICTION models FREQUENCY spectra
Zdroj:	Journal of Earthquake Engineering; Nov2022, Vol. 26 Issue 14, p7319-7336, 18p
Abstrakt:	This research proposes a surrogate model to predict the seismic response of individual structural elements in structures whose inherent vertical and horizontal irregularities result in components with different seismic vulnerabilities. A frequency-based data-driven model was developed which predominantly uses the frequency spectrum of earthquakes as input data. The seismic responses of several structural components can be simultaneously generated as output using the proposed model. A comparison of structure fragility assessments obtained with a conventional approach, and the proposed Deep Learning-based approach, was conducted to verify the accuracy of the proposed method's prediction capability. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
Externí odkaz:	Zobrazit plný text záznamu