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首页> 外文期刊>Electronic Journal of Structural Engineering >Damage Identification of a Concrete Arch Beam Based on Frequency Response Functions and Artificial Neural Networks
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Damage Identification of a Concrete Arch Beam Based on Frequency Response Functions and Artificial Neural Networks

机译：基于频率响应函数和人工神经网络的混凝土拱梁损伤识别

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This paper presents a vibration-based structural health monitoring (SHM) technique for the identification of damage in a concrete arch beam replica section of the Sydney Harbour Bridge. The proposed technique uses residual frequency response functions (FRFs) combined with principal component analysis (PCA) to form a damage specific feature (DSF) that is used as an input parameter to artificial neural networks (ANNs). Extensive laboratory testing and numerical modelling are undertaken to validate the method. In the proposed technique, FRFs are obtained by the standard modal testing and damage is identified using ANNs that innovatively map the DSF to the severity of damage (length of damage cut). The results of the experi- mental and numerical validation show that the proposed technique can successfully quantify damage induced to a concrete arch beam simulating a real life structural component of the Sydney Harbour Bridge.
机译：本文提出了一种基于振动的结构健康监测（SHM）技术，用于识别悉尼海港大桥的混凝土拱形梁复制段中的损伤。所提出的技术结合了剩余频率响应函数（FRF）和主成分分析（PCA）来形成特定于损伤的特征（DSF），将其用作人工神经网络（ANN）的输入参数。进行了广泛的实验室测试和数值建模，以验证该方法。在提出的技术中，通过标准模态测试获得FRF，并使用ANN识别损坏，而ANN将DSF创新地映射到损坏的严重程度（切割长度）。实验和数值验证的结果表明，所提出的技术可以成功地模拟混凝土拱桥梁对悉尼港湾大桥的真实结构部件的损伤。

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