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Time and Frequency Approaches to Non Destructive Testing in Concrete Pillars Using Neural Networks

机译:基于神经网络的混凝土柱无损检测的时间和频率方法

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In this paper, Multi Layer Perceptron neural networks have been trained to identify the position of defects in concrete structures analyzed using an ultrasound technique. A diagnostic model obtained by means of Finite Elements techniques has been used to model the ultrasound transmission through a concrete pillar of specified size affected by defects in different positions. The obtained signals have been processed both in the time and frequency domains, in order to reduce data dimensionality and to compute suitable features. Results show good accuracy in the identification of the position of the faults.
机译:在本文中,已经训练了多层的Perceptron神经网络,以识别使用超声技术分析的混凝土结构中缺陷的位置。通过有限元技术获得的诊断模型已被用于通过在不同位置缺陷影响的特定尺寸的混凝土支柱来模拟超声波传输。已经在时间和频率域中处理了所获得的信号,以减少数据维度并计算合适的特征。结果在识别故障位置时显示出良好的准确性。

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