Damage identification analysis of Cable-stayed arch-truss based on multi-node time -domain data fusion

Abstract The potential risk of cable-stayed arch-truss damage is large and the damage is undetectable. The damage identification methods based on frequency domain have limitations such as limited data and complex theoretical methods. A damage identification method based on multi-node time-domain data fusion was proposed to overcome these limitations. The time-domain data library was established by finite element analysis, and the time-domain data was preprocessed and augmented. Two CNNs models were established to identify the damage location and damage degree of cable-stayed arch-truss. The proposed method was verified by the analysis of a practical cable-stayed arch-truss scale model, and the recognition effect of the method on noisy data and noise-free data was studied respectively. The results showed that the CNN can effectively identify the damage degree and damage location of cable-stayed arch-truss structure with good robustness. CNN with Gaussian noise can accurately predict the damage degree of cable-stayed arch-truss. The prediction error of most elements is within 15%, which can meet the actual needs of engineering.摘要 斜拉拱桁结构的损伤具有较高的潜在风险，且此类损伤难以被检测到。现有基于频域的损伤识别方法存在数据样本有限、理论流程复杂等局限性。为克服上述局限，本文提出了一种基于多节点时域数据融合的损伤识别方法。通过有限元分析构建时域数据库，并对时域数据开展预处理与数据增强操作。构建了两个卷积神经网络（Convolutional Neural Networks, CNN）模型，用于识别斜拉拱桁结构的损伤位置与损伤程度。通过某实物斜拉拱桁缩尺模型的分析对所提方法进行了验证，并分别研究了该方法在含噪数据与无噪数据下的识别效果。结果表明，该卷积神经网络可有效识别斜拉拱桁结构的损伤程度与损伤位置，且具备良好的鲁棒性。在含高斯噪声的场景下，该网络仍可精准预测斜拉拱桁结构的损伤程度，多数构件的预测误差控制在15%以内，可满足工程实际应用需求。