Modelling non-linear aeroelastic loads in long-span bridges with extreme learning machines
Accurate modelling of aerodynamic loads is essential for predicting instabilities and ensuring the safety of long-span bridges. A methodology is introduced for modelling aerodynamic self-excited forces in bridge-deck cross-sections using extreme learning machines (ELMs). ELMs, as single-layer feedforward neural networks, offer efficient training and accurate predictions. Forced-oscillation datasets from computational fluid dynamics (CFD) or wind-tunnel experiments are used for training, enabling systematic data selection to capture non-linear aerodynamic behaviour often missed by semi-analytical approaches. Once trained, the model predicts self-excited loads for any arbitrary motion compo...
| Authors | Gledson Rodrigo Tondo, Samir Chawdhury, Sergio Andres Castro Giraldo et al. |
|---|---|
| Primary category | cs.CE |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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