基于神经网络辅助电阻抗断层成像的自感知材料的实时精确损伤表征:一项计算研究

Lin, Guang, Zhao, Lang, Tallman, Tyler
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引用次数: 5

摘要

许多案例证明了结构健康监测(SHM)战略的重要性,这种战略可以检测基础设施或建筑物的结构健康状况,以防止潜在的经济或人员损失。纳米复合材料,如碳纳米填料改性复合材料,由于其压阻性,在SHM中具有很大的潜力。因此,通过研究材料的电导率变化分布来确定材料的损伤状态是可能的,这对于检测肉眼无法观察到的位置,例如翼型内层的损伤是必不可少的。目前,许多研究者对损伤对纳米复合材料电导率的影响进行了研究,电阻抗层析成像(EIT)方法已广泛应用于检测损伤引起的电导率变化。然而,对于SHM来说,仅仅知道如何计算损伤后的电导率变化是不够的,知道如何确定导致观察到的电导率变化的机械损伤对SHM来说更有价值。在本文中,我们通过研究EIT方法生成的电导率变化数据,应用机器学习方法来确定材料试样上的损伤状态,即破碎孔的数量、半径和中心位置。我们的研究结果表明,机器学习方法可以通过分析电导率变化数据来准确有效地检测材料样品的损伤,这一结论对SHM领域具有重要意义,并将加快航空工业和机械工程等行业的损伤检测过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-Time Precise Damage Characterization in Self-Sensing Materials via Neural Network-Aided Electrical Impedance Tomography: A Computational Study
Many cases have evinced the importance of having structural health monitoring (SHM) strategies that can allow the detection of the structural health of infrastructures or buildings, in order to prevent the potential economic or human losses. Nanocomposite material like the Carbon nanofiller-modified composites have great potential for SHM because these materials are piezoresistive. So, it is possible to determine the damage status of the material by studying the conductivity change distribution, and this is essential for detecting the damage on the position that can-not be observed by eye, for example, the inner layer in the aerofoil. By now, many researchers have studied how damage influences the conductivity of nanocomposite material and the electrical impedance tomography (EIT) method has been applied widely to detect the damage-induced conductivity changes. However, only knowing how to calculate the conductivity change from damage is not enough to SHM, it is more valuable to SHM to know how to determine the mechanical damage that results in the observed conductivity changes. In this article, we apply the machine learning methods to determine the damage status, more specifically, the number, radius and the center position of broken holes on the material specimens by studying the conductivity change data generated by the EIT method. Our results demonstrate that the machine learning methods can accurately and efficiently detect the damage on material specimens by analysing the conductivity change data, this conclusion is important to the field of the SHM and will speed up the damage detection process for industries like the aviation industry and mechanical engineering.
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