Abstract This paper addresses a damage detection method based on changes in modal curvature combined with Convolutional Neural Network (CNN). The use of modal curvature for damage detection is very well known. Some methods require modal data of healthy structures as a reference, others do not. Gapped Smoothing Method (GSM) is a vibration-based damage detection methodology, which makes use of modal curvature for identifying the location of structural damage and does not require information of the intact structure. The Bo Nghi bridge is used as an illustrative example. This bridge consists of four T-shaped concrete simply supported girders. One single beam with the same length and cross-section of the bridge girder is modeled and used to extract numerical data to train the CNN. A CNN is trained by using images from the damage index of the GSM to classify the damage location in the numerical beam model. Finally, the finite element model of the bridge is built and used to model damage scenarios to test the trained CNN. The results indicate that the combination of GSM and CNN can be used for damage detection and localization.

中文翻译：

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使用模态曲率间隙平滑方法和卷积神经网络在梁桥中进行损伤检测：在 Bo Nghi 桥上的应用

摘要 本文提出了一种基于模态曲率变化结合卷积神经网络 (CNN) 的损伤检测方法。使用模态曲率进行损伤检测是众所周知的。有些方法需要健康结构的模态数据作为参考，有些则不需要。Gapped Smoothing Method (GSM) 是一种基于振动的损伤检测方法，它利用模态曲率来识别结构损伤的位置，不需要完整结构的信息。以博义桥为例。这座桥由四根T形混凝土简支梁组成。对具有相同长度和横截面的桥梁的一根单梁进行建模，并用于提取数值数据以训练 CNN。通过使用来自 GSM 损伤指数的图像来训练 CNN，以对数值梁模型中的损伤位置进行分类。最后，建立桥梁的有限元模型并用于模拟损坏场景以测试训练的 CNN。结果表明GSM和CNN的结合可以用于损伤检测和定位。