Engineers often heavily rely on wind tunnel tests or computational fluid dynamics (CFD) to evaluate the flutter performance of bridges in their preliminary design, which is costly and time-consuming. To quickly obtain the critical flutter wind speed of streamlined box girders in the preliminary design, a machine learning (ML) strategy was proposed in this paper. A big dataset was built by testing critical flutter wind speeds of 30 sectional models of streamlined box girders with and without railings at 5 angles of attack through free vibration wind tunnel tests. The flutter predicting models, taking geometric information and dynamic parameters as inputs, were built based on four widely-used ML algorithms, i.e., support vector regression (SVR), neural network (NN), random forest regression (RFR), and gradient boosting regression tree (GBRT). It is shown that the NN and GBRT models exhibit the highest prediction accuracy for the girders regardless of railings, respectively. A comparative study revealed that the ML models were superior over those simplified formulas for flutter estimation including the Van der Put formula, Selberg formula, and Haifan Xiang formula. A case study was also given to demonstrate the practical application of the proposed method. These ML models provide an efficient supplement to wind tunnel tests and CFD simulations for flutter predictions of streamlined box girders in the preliminary design.

工程师在其初步设计中经常严重依赖风洞测试或计算流体力学（CFD）来评估桥梁的颤振性能，这既昂贵又费时。为了在初步设计中快速获得流线型箱梁的临界扑动风速，本文提出了一种机器学习（ML）策略。通过自由振动风洞测试，通过测试30种流线型箱梁截面模型的关键颤振风速，在5个迎角下使用和不使用栏杆，来建立大型数据集。基于几何信息和动态参数作为输入的颤振预测模型是基于四种广泛使用的ML算法建立的，即支持向量回归（SVR），神经网络（NN），随机森林回归（RFR）和梯度提升回归树（GBRT）。结果表明，无论栏杆如何，NN和GBRT模型分别对梁的预测精度最高。一项比较研究表明，ML模型优于颤振估计的简化公式，包括Van der Put公式，Selberg公式和Haifan Xiang公式。案例研究也证明了该方法的实际应用。这些ML模型为风洞试验和CFD模拟提供了有效的补充，用于初步设计中流线型箱梁的颤振预测。一项比较研究表明，ML模型优于颤振估计的简化公式，包括Van der Put公式，Selberg公式和Haifan Xiang公式。案例研究也证明了该方法的实际应用。这些ML模型为风洞试验和CFD模拟提供了有效的补充，用于初步设计中流线型箱梁的颤振预测。一项比较研究表明，ML模型优于颤振估计的简化公式，包括Van der Put公式，Selberg公式和Haifan Xiang公式。案例研究也证明了该方法的实际应用。这些ML模型为风洞试验和CFD模拟提供了有效的补充，用于初步设计中流线型箱梁的颤振预测。