Machine learning and finite element integration-driven surrogate model for fluid-structure interaction seismic response analysis of aqueduct structures

•Methodological Innovation: A synergistic TFSI modeling framework integrates multiphysics simulations and geometric parameterization, trained with 12,600 datasets.•Algorithm Advancement: The improved sand cat swarm optimization algorithm (ISCSOBP) achieves 78 % higher accuracy than traditional BP ne...

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Veröffentlicht in:	Results in engineering Jg. 27; S. 106176
Hauptverfasser:	Huang, Liang, Li, Ge, Guan, Yujian, Jiao, Weili, Gong, Shengjia, Xu, Shizhan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.09.2025
Schlagworte:	Aqueduct Machine learning Seismic response Surrogate model Two-way fluid-structure interaction Aqueduct Two-way fluid-structure interaction Surrogate model Seismic response Machine learning
ISSN:	2590-1230, 2590-1230
Online-Zugang:	Volltext
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