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首页> 外文期刊>International journal of steel structures >Pipe Crack Identification Based on the Energy Method and Committee of Neural Networks
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Pipe Crack Identification Based on the Energy Method and Committee of Neural Networks

机译：基于能量法和神经网络委员会的管道裂纹识别

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摘要

A crack identification method using an equivalent bending stiffness for cracked beam and committee of neural networks is presented. The equivalent bending stiffness is constructed based on an energy method for a straight thin-walled pipe, which has a through-the-thickness crack, subjected to bending. Several numerical analysis for a steel cantilever pipe using the equivalent bending stiffness are carried out to extract the natural frequencies and mode shapes of the cracked beam. The extracted modal properties are used in constructing a training patterns of a neural network. The input to the neural network consists of the modal properties and the output is composed of the crack location and size. Multiple neural networks are constructed and each individual network is trained independently with the different initial synaptic weights. Then, the estimated crack locations and sizes from different neural networks are averaged. Crack detection is carried out for 16 damage cases using the proposed method, and the identified crack locations and sizes agree reasonably well with the exact values.
机译：提出了一种基于等效弯曲刚度的裂纹识别方法及神经网络委员会。等效弯曲刚度是基于能量方法构造的，对于具有弯曲厚度的全壁直管，该直管是薄壁管。使用等效弯曲刚度对悬臂钢管进行了一些数值分析，以提取裂隙梁的固有频率和振型。提取的模态属性用于构建神经网络的训练模式。神经网络的输入由模态属性组成，输出由裂纹的位置和大小组成。构建了多个神经网络，并使用不同的初始突触权重独立训练每个单独的网络。然后，对来自不同神经网络的估计裂纹位置和大小进行平均。使用所提出的方法对16个损坏情况进行了裂纹检测，并且确定的裂纹位置和大小与精确值相当吻合。

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