There are large amounts of small-and medium-span girder bridges which bear structural similarity, while the large-scale bridge structures are generally limited in the timely applications of structural vibration characteristics. Therefore, in this study a framework based on machine learning models was proposed to analyze the vibration characteristics of specific line bridge groups. The probability distributions of structural, geometric, and material properties of bridge groups in specific lines were obtained using statistical tools and a Latin hypercube sampling method was used to generate reasonable sample sets for the bridges group, and parameterized finite element models of the bridges were established. Then, the optimal models were tuned and determined to predict fundamental mode and period by the 10-fold cross-validation method applying the numerical simulation results. This study’s results showed that the random forest models divided the vibration modes of the bridge groups into the longitudinal vibrations of the main girders and the longitudinal vibrations of the adjacent spans and side piers with a classification accuracy of greater than 90%, while the artificial neural network models exhibited the lowest normalized mean square error for the periods. The periods mainly ranged between 0.7 and 1.5 s. Furthermore, the bearing settings, ratios of the pier height to section diameters, and boundary types were determined to be the most significant properties influencing the fundamental modes and periods of the examined bridges, by respectively observing the reduced value of the random forest Gini indices and distribution of the generalized weight value of the input variables in artificial neural networks. This study provides an intelligent and efficient method for obtaining vibration characteristics of bridges group for a specific network.

大中小跨度的桥梁结构相似，而大型桥梁的结构通常在结构振动特性的及时应用上受到限制。因此，在这项研究中，提出了一个基于机器学习模型的框架来分析特定线桥组的振动特性。使用统计工具获得特定线段桥梁组的结构，几何和材料特性的概率分布，并使用拉丁超立方体抽样方法为桥梁组生成合理的样本集，并建立桥梁的参数化有限元模型。然后，通过使用数值模拟结果的10倍交叉验证方法，对最佳模型进行了调整和确定，以预测基本模式和周期。研究结果表明，随机森林模型将桥梁群的振动模式分为主梁的纵向振动和相邻跨度和侧墩的纵向振动，其分类精度大于90％，而人工神经网络网络模型在各个时期内显示出最低的标准化均方误差。周期主要在0.7到1.5 s之间。此外，确定轴承设置，桥墩高度与截面直径的比率以及边界类型是影响所检查桥梁的基本模式和周期的最重要属性，通过分别观察随机森林基尼系数的减少值和人工神经网络中输入变量的广义权重值的分布。该研究为获得特定网络桥梁组的振动特性提供了一种智能，有效的方法。