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Data-Driven Substructuring Technique for Pseudo-Dynamic Hybrid Simulation of Steel Braced Frames
https://orcid.org/http://orcid.org/0000-0001-7385-0098
This paper proposes a new substructuring technique for hybrid simulation of steel braced frame structures under seismic loading in which a new machine learning-based model is used to predict the hysteretic response of steel braces. Corroborating numerical data is used to train the model, referred to as PI-SINDy, developed with the aid of the Prandtl-Ishlinskii hysteresis model and sparse identification algorithm. By replacing a brace part of a prototype steel buckling-restrained braced frame with the trained PI-SINDy model, a new simulation technique referred to as data-driven hybrid simulation (DDHS) is established. The accuracy of DDHS is evaluated using the nonlinear response history analysis of the prototype frame subjected to an earthquake ground motion. Compared to a baseline pure numerical model, the results show that the proposed model can accurately predict the hysteretic response of steel buckling-restrained braces.
Data-Driven Substructuring Technique for Pseudo-Dynamic Hybrid Simulation of Steel Braced Frames
https://orcid.org/http://orcid.org/0000-0001-7385-0098
This paper proposes a new substructuring technique for hybrid simulation of steel braced frame structures under seismic loading in which a new machine learning-based model is used to predict the hysteretic response of steel braces. Corroborating numerical data is used to train the model, referred to as PI-SINDy, developed with the aid of the Prandtl-Ishlinskii hysteresis model and sparse identification algorithm. By replacing a brace part of a prototype steel buckling-restrained braced frame with the trained PI-SINDy model, a new simulation technique referred to as data-driven hybrid simulation (DDHS) is established. The accuracy of DDHS is evaluated using the nonlinear response history analysis of the prototype frame subjected to an earthquake ground motion. Compared to a baseline pure numerical model, the results show that the proposed model can accurately predict the hysteretic response of steel buckling-restrained braces.
Data-Driven Substructuring Technique for Pseudo-Dynamic Hybrid Simulation of Steel Braced Frames
https://orcid.org/http://orcid.org/0000-0001-7385-0098
This paper proposes a new substructuring technique for hybrid simulation of steel braced frame structures under seismic loading in which a new machine learning-based model is used to predict the hysteretic response of steel braces. Corroborating numerical data is used to train the model, referred to as PI-SINDy, developed with the aid of the Prandtl-Ishlinskii hysteresis model and sparse identification algorithm. By replacing a brace part of a prototype steel buckling-restrained braced frame with the trained PI-SINDy model, a new simulation technique referred to as data-driven hybrid simulation (DDHS) is established. The accuracy of DDHS is evaluated using the nonlinear response history analysis of the prototype frame subjected to an earthquake ground motion. Compared to a baseline pure numerical model, the results show that the proposed model can accurately predict the hysteretic response of steel buckling-restrained braces.
Data-Driven Substructuring Technique for Pseudo-Dynamic Hybrid Simulation of Steel Braced Frames
Lecture Notes in Civil Engineering
Mazzolani, Federico M. (editor) / Dubina, Dan (editor) / Stratan, Aurel (editor) / Mokhtari, Fardad (author) / Imanpour, Ali (author)
International Conference on the Behaviour of Steel Structures in Seismic Areas ; 2022 ; Timisoara, Romania
2022-05-08
9 pages
Article/Chapter (Book)
Electronic Resource
English
Data-Driven Substructuring Technique for Pseudo-Dynamic Hybrid Simulation of Steel Braced Frames
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