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Damage identification of urban overpass based on modal frequency and genetic neural network

机译:基于模态频率和遗传神经网络的城市立交桥损伤识别

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The finite element model of left auxiliary bridge of Qianjin Overpass is built and vulnerable sections of structure are chosen as research objects. In consideration of the asymmetry of the bridge, change rate of modal frequency is chosen as input parameter for genetic neural network, and identification ability of damage location and level is studied. The result shows that this method can successfully identify location of single damage and multi-damage; The error of damage level identification for test samples is less than 5% and the interpolation ability is better than the extrapolation ability. This indicates the method has good practice prospects.
机译:Qianjin立交桥左辅助桥的有限元模型是建造的,构建脆弱的结构部分作为研究对象。 考虑到桥的不对称性,选择模频频率的变化率作为基因神经网络的输入参数,研究了损坏位置和水平的识别能力。 结果表明,该方法可以成功识别单一损坏和多损坏的位置; 用于测试样品的损伤水平识别误差小于5%,插值能力优于外推能力。 这表明该方法具有良好的实践前景。

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