Long-span cable-supported bridges are structures susceptible to high dynamic responses due to the buffeting phenomenon. The current state-of-the-art method for buffeting response estimation is the buffeting theory. However, previous research has shown discrepancies between buffeting theory estimates and full-scale measured response, revealing a weakness in the theoretical models. In cases where wind and structural health monitoring data are available, machine learning algorithms may enhance the buffeting response estimation speed with less computational effort by bypassing the analytical model’s assumptions. In this paper, multilayer perceptron and support vector regression models were trained with synthetic and full-scale measured data from the Hardanger Bridge. The analytical response was also computed from buffeting theory applied to a finite element model of the bridge, and the estimates are compared. The prediction accuracy was evaluated with the normalized root mean square error, the mean absolute percent error and the coefficient of determination (R2). The machine learning models trained with synthetic datasets achieved very high accuracy with normalized root mean square errors ranging from 1.46E-04 to 7.21E-03 and are therefore suitable for efficient surrogate modeling. Further, the support vector regression model trained with the full-scale measured dataset achieved the best accuracy compared with the other methods.

大跨度电缆支撑桥是一种因抖振现象而易于产生高动态响应的结构。抖振响应估计是当前最先进的方法。但是，先前的研究表明，抖振理论估计值与实测响应之间存在差异，这表明理论模型存在缺陷。在有风和结构健康监测数据的情况下，机器学习算法可以绕过分析模型的假设，从而以较少的计算量来提高抖振响应估计速度。在本文中，使用来自Hardanger桥的合成和全面测量数据训练了多层感知器和支持向量回归模型。还根据应用于桥梁有限元模型的抖振理论计算了分析响应，并对估计值进行了比较。通过归一化均方根误差，平均绝对百分比误差和确定系数（R2）评估预测准确性。使用合成数据集训练的机器学习模型实现了非常高的精度，归一化均方误差在1.46E-04到7.21E-03之间，因此适用于有效的替代模型。此外，与其他方法相比，使用全面测量的数据集训练的支持向量回归模型实现了最佳准确性。平均绝对百分比误差和确定系数（R2）。使用合成数据集训练的机器学习模型实现了非常高的精度，归一化均方误差在1.46E-04到7.21E-03之间，因此适用于有效的替代模型。此外，与其他方法相比，使用全面测量的数据集训练的支持向量回归模型实现了最佳准确性。平均绝对百分比误差和确定系数（R2）。使用合成数据集训练的机器学习模型实现了非常高的精度，归一化均方误差在1.46E-04到7.21E-03之间，因此适用于有效的替代模型。此外，与其他方法相比，使用全面测量的数据集训练的支持向量回归模型实现了最佳准确性。