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Damage identification for beams using ANN based on statistical property of structural responses

机译:基于结构响应统计特性的神经网络损伤识别

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摘要

In this paper, a novel method of damage identification for beam using artificial neural network (ANN) based on statistical properties of structural dynamic responses is developed. In this method, the changes of variances (or covariance) of structural responses are selected as damage indices for damage identification. Firstly, the feasibility of using the statistical property as damage index is validated theoretically with sensitivity analysis. Then the back-propagation ANN with the change of variance of structural response as input and damage status as output is adopted for identifying the damage in beams. The damage identification for a three-span continuous beam using the developed method is numerically simulated. From the results of numerical simulation in both single damage case and multi-damage case, it is found that the ANN with the statistical property as damage index can correctly detect the damage location and identify the damage extent with high precision. Finally, the conclusion is given that the novel method using the statistical property of structural response as damage index for damage identification is feasible and efficient.
机译:本文基于结构动力响应的统计特性,提出了一种基于人工神经网络的梁损伤识别新方法。在这种方法中,选择结构响应的方差(或协方差)变化作为损伤识别的损伤指标。首先,通过敏感性分析从理论上验证了将统计特性作为损伤指标的可行性。然后采用结构响应方差变化作为输入而损伤状态作为输出的反向传播神经网络来识别梁的损伤。数值模拟了采用所开发方法对三跨连续梁的损伤识别。从单损伤情况和多损伤情况的数值模拟结果可以看出,以统计特性为损伤指标的人工神经网络可以正确地检测出损伤位置,并能高精度地识别出损伤程度。最后得出结论,利用结构响应统计特性作为损伤指标进行损伤识别的新方法是可行,有效的。

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