Bayesian-optimized interpretable surrogate model for seismic demand prediction of urban highway bridges

Lei, Xiaoming; Feng, Ruiwei; Dong, You; Zhai, Changhai

doi:10.1016/j.engstruct.2023.117307

Cited by 18 publications

(1 citation statement)

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“…Advanced data-driven methods such as neural networks model the dynamic mechanism of the system by mining the relationship embedded in the data of the input and output [19,20]. Without the limitation of various assumptions, data-driven methods can be more flexible and more easily capture the complex mechanism such as seismic demand [21], vehicle-bridge interaction [22] and the aerodynamic system [23][24][25][26]. Some research has been conducted in the field of parameter identification.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Model for Predictive Modeling of Vortex-Induced Vibrations of a Long-Span Bridge

Wang,

Feng,

et al. 2024

Applied Sciences

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Vortex-induced vibration (VIV) of long-span bridges can be of large amplitude, which can influence serviceability. Therefore, it is important to predict the response of vortex-induced vibration to aid the management of long-span bridges. A novel data-driven model is proposed to predict the time history of the dynamic response of VIV events. Specifically, the proposed model consists of gated recurrent unit (GRU) neural networks and the Newmark-beta method. GRU neural networks can perform accurate sequential prediction, and the Newmark-beta method can complement the physical meaning of the middle output of the proposed model. To aid the accurate prediction of the amplitude of VIV events, the proposed model employs weighted mean square error as the loss function, which can put more emphasis on the amplitude. The proposed model is validated on measured VIV events of a long-span suspension bridge. The weighted mean absolute percentage error and Pearson correlation coefficient of the trained model indicate the effectiveness of the proposed model.

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