SummaryFlexibility is an important parameter reflecting bridge load‐carrying capacity. Dynamic testing is a fast and effective method to obtain the structural modal flexibility of small‐ and medium‐span bridges. The Deterministic‐stochastic subspace identification (DSI) algorithm is a well‐established structure identification method in the time domain. However, the estimation of damping, especially the modal scaling factor, is not always reliable due to inevitable measurement noise, which directly affects the identification accuracy of flexibility. This paper proposes a maximum likelihood estimation method in subbands (SMLE), which can be regarded as an add‐on method of the DSI algorithm because the initial parameters are obtained from the DSI algorithm. The processing of frequency band division is implemented first, and the frequency response function curve in each subband of the whole frequency range is fit separately. Then, subband cyclic iteration is proposed to improve the identification accuracy in a closely spaced mode system. The proposed SMLE method maintains the advantages of the DSI algorithm while improving the accuracy of parameter estimation and flexibility identification. Two lumped mass models are used to verify that the proposed method can effectively improve estimates to obtain a precise flexibility matrix and predict the displacement of the structure under static loading. Experimental example of a continuous girder bridge is considered to verify the availability and effectiveness of the proposed method in practice.

中文翻译：

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通过子带估计方法从冲击测试数据中识别结构灵活性

小结柔度是反映桥梁承载能力的重要参数。动态试验是获取中小跨桥梁结构模态柔度的快速有效的方法。确定性随机子空间识别（DSI）算法是一种成熟的时域结构识别方法。然而，由于不可避免的测量噪声，阻尼尤其是模态比例因子的估计并不总是可靠，这直接影响了柔性的识别精度。本文提出了子带最大似然估计方法（SMLE），该方法可以看作是DSI算法的附加方法，因为初始参数是从DSI算法获得的。首先进行频段划分处理，分别拟合全频段各子带的频率响应函数曲线。然后，提出子带循环迭代来提高紧密间隔模式系统中的识别精度。所提出的SMLE方法保持了DSI算法的优点，同时提高了参数估计和灵活性识别的准确性。使用两个集中质量模型验证了所提出的方法可以有效地改进估计以获得精确的柔度矩阵并预测结构在静载下的位移。考虑连续梁桥的实验实例，验证了该方法在实践中的可用性和有效性。