The vibration-based damage identification techniques use changes in modal properties of structures to detect damages. However, the results of these methods are not reliable under noise. Therefore, it is essential to clarify which method performs vigorous under noisy conditions. In this study, three damage detection methods, called modal strain energy-based damage index, modal flexibility, and modal curvature, are considered to detect damage with and without the presence of noise. The feasibility of these methods is demonstrated by applying a range of damage scenarios in the validated FE model of the I-40 Bridge. The info of the only first three bending mode shapes of the bridge is used to calculate damage indices. The outcome showed while all three methods were capable of detecting damage in the absence of noise, only the modal flexibility method could locate damages in the presence of noise. Thus, an approach is proposed to eliminate noise and quantify damage magnitude using an artificial neural network (ANN) and modal flexibility method. The modal flexibility damage index of different damage severities was contaminated with various noise levels used as input parameters to train the ANN. Results indicate the adequate performance of the trained ANN in noise-canceling and damage magnitude estimation.

中文翻译：

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噪声条件下使用人工神经网络的钢梁桥振动损伤识别

基于振动的损伤识别技术利用结构模态特性的变化来检测损伤。然而，这些方法的结果在噪声下并不可靠。因此，必须明确哪种方法在嘈杂条件下表现强劲。在这项研究中，三种损伤检测方法，称为基于模态应变能的损伤指数、模态柔度和模态曲率，被认为可以检测存在和不存在噪声的损伤。这些方法的可行性通过在 I-40 大桥经过验证的有限元模型中应用一系列损坏场景来证明。桥梁的前三个弯曲模式形状的信息用于计算损伤指数。结果表明，虽然所有三种方法都能够在没有噪音的情况下检测到损坏，只有模态柔度法才能在有噪声的情况下定位损伤。因此，提出了一种使用人工神经网络 (ANN) 和模态灵活性方法来消除噪声和量化损伤幅度的方法。不同损伤严重程度的模态柔性损伤指数受到各种噪声水平的污染，这些噪声水平用作训练 ANN 的输入参数。结果表明，经过训练的人工神经网络在噪声消除和损伤幅度估计方面具有足够的性能。