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On the processing of highly nonlinear solitary waves and guided ultrasonic waves for structural health monitoring and nondestructive evaluation.

机译：用于处理高度非线性的孤立波和引导超声波，以进行结构健康监测和无损评估。

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摘要

The in-situ measurement of thermal stress in civil and mechanical structures may prevent structural anomalies such as unexpected buckling. In the first half of the dissertation, we present a study where highly nonlinear solitary waves (HNSWs) were utilized to measure axial stress in slender beams. HNSWs are compact non-dispersive waves that can form and travel in nonlinear systems such as one-dimensional chains of particles. The effect of the axial stress acting in a beam on the propagation of HNSWs was studied. We found that certain features of the solitary waves enable the measurement of the stress.;In general, most guided ultrasonic waves (GUWs)-based health monitoring approaches for structural waveguides are based on the comparison of testing data to baseline data. In the second half of the dissertation, we present a study where some baseline-free signal processing algorithms were presented and applied to numerical and experimental data for the structural health monitoring (SHM) of underwater or dry structures. The algorithms are based on one or more of the following: continuous wavelet transform, empirical mode decomposition, Hilbert transform, competitive optimization algorithm, probabilistic methods. Moreover, experimental data were also processed to extract some features from the time, frequency, and joint time-frequency domains. These features were then fed to a supervised learning algorithm based on artificial neural networks to classify the types of defect. The methods were validated using the numerical model of a plate and a pipe, and the experimental study of a plate in water. In experiment, the propagation of ultrasonic waves was induced by means of laser pulses or transducer and detected with an array of immersion transducers. The results demonstrated that the algorithms are effective, robust against noise, and able to localize and classify the damage.
机译：土木和机械结构中热应力的现场测量可以防止结构异常，例如意外屈曲。在论文的前半部分，我们进行了一项研究，其中利用高度非线性的孤立波（HNSW）来测量细长梁的轴向应力。 HNSW是紧凑的非分散波，可以在非线性系统（例如一维粒子链）中形成并传播。研究了梁中的轴向应力对HNSWs传播的影响。我们发现，孤立波的某些特征可以测量应力。通常，大多数基于波导（GUWs）的结构波导健康监测方法都是基于测试数据与基准数据的比较。在论文的后半部分，我们提出了一项研究，其中提出了一些无基线的信号处理算法，并将其应用于数值和实验数据，以进行水下或干燥结构的结构健康监测（SHM）。该算法基于以下一项或多项：连续小波变换，经验模式分解，希尔伯特变换，竞争性优化算法，概率方法。此外，还对实验数据进行了处理，以从时域，频域和联合时频域中提取一些特征。然后将这些特征输入到基于人工神经网络的监督学习算法中，以对缺陷类型进行分类。使用板和管的数值模型以及在水中的板的实验研究对方法进行了验证。在实验中，超声波的传播是通过激光脉冲或换能器引起的，并通过一系列浸没式换能器进行检测。结果表明，该算法有效，鲁棒，抗噪声，并且能够对损伤进行定位和分类。

著录项

  • 作者

    Bagheri, Abdollah.;

  • 作者单位

    University of Pittsburgh.;

  • 授予单位 University of Pittsburgh.;
  • 学科 Civil engineering.;Mechanical engineering.;Materials science.
  • 学位 Ph.D.
  • 年度 2015
  • 页码 326 p.
  • 总页数 326
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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