基于极限学习机的大跨桥梁非线性气动弹性荷载建模
Modelling non-linear aeroelastic loads in long-span bridges with extreme learning machines
- Bauhaus-Universität Weimar（魏玛包豪斯大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出利用极限学习机高效准确建模大跨桥梁非线性气动自激力，训练时间约为传统神经网络的1.1%，耦合颤振分析秒级完成，精度优于半解析方法。
AI中文摘要：
准确建模气动荷载对于预测失稳和确保大跨桥梁的安全至关重要。本文提出了一种利用极限学习机（ELMs）对桥梁主梁截面气动自激力进行建模的方法。ELMs作为单层前馈神经网络，具有训练高效和预测准确的优点。训练数据来自计算流体力学（CFD）或风洞试验的强迫振动数据集，能够系统地选择数据以捕捉半解析方法经常遗漏的非线性气动行为。训练完成后，该模型能够预测训练域内任意由频率和振幅组合而成的运动所对应的自激荷载。与解析法、半解析法和CFD结果的比较表明，该方法在捕捉非线性力分量方面具有优越的准确性，并且在气动荷载和颤振风速方面与参考结果高度一致。训练时间约为传统神经网络的1.1%，耦合颤振分析可在数秒内完成，相比CFD实现了数量级的加速。这些结果表明，基于ELM的框架是建模自激荷载的准确、实用且高效的替代方案，尤其是在已有初步CFD或风洞数据的情况下。所提出的方法为大跨桥梁气动弹性荷载建模提供了一种可靠的数据驱动技术。
英文摘要：
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 composed by frequencies and amplitudes within the training domain. Comparisons with analytical, semi-analytical, and CFD results show superior accuracy in capturing non-linear force components and close agreement for aerodynamic loads and flutter wind speeds. Training required about 1.1\% of the time of a conventional neural network, and coupled flutter analysis runs in seconds, providing orders-of-magnitude speed-ups over CFD. These results indicate that ELM-based frameworks are accurate, practical, and efficient alternatives for modelling self-excited loads, particularly when preliminary CFD or wind-tunnel data are available. The presented approach offers a reliable data-driven technique for aeroelastic load modelling in long-span bridges.
