Analytische Chemie
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- Nondestructive testing (NDT) (2) (entfernen)
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The changes in the sonic surface wave velocity of concrete under stress were investigated in this paper. Surface wave velocities at
sonic frequency range were measured on a prismatic concrete specimen undergoing several cycles of uniaxial compression. The
loading was applied (or removed) gradually in predefined small steps (stress-controlled). The surface wave velocity was measured
at every load step during both loading and unloading phases. Acoustic Emission (AE) test was conducted simultaneously to
monitor the microcracking activities at different levels of loading. It was found that the sonic surface wave velocity is highly stress
dependent and the velocity-stress relationship follows a particular trend. The observed trend could be explained by a combination
of acoustoelasticity and microcracking theories, each valid over a certain range of applied stresses. Having measured the velocities
while unloading, when the material suffers no further damage, the effect of stress and damage could be differentiated. The slope
of the velocity-stress curves over the elastic region was calculated for different load cycles. This quantity was normalized to yield a
dimensionless nonlinear parameter. This parameter generally increases with the level of induced damage in concrete.
This article presents deep unfolding neural networks to handle inverse problems in photothermal radiometry enabling super-resolution (SR) imaging. The photothermal SR approach is a well-known technique to overcome the spatial resolution limitation in photothermal imaging by extracting high-frequency spatial components based on the deconvolution with the thermal point spread function (PSF). However, stable deconvolution can only be achieved by using the sparse structure of defect patterns, which often requires tedious, handcrafted tuning of hyperparameters and results in computationally intensive algorithms. On this account, this article proposes Photothermal-SR-Net, which performs deconvolution by deep unfolding considering the underlying physics. Since defects appear sparsely in materials, our approach includes trained block-sparsity thresholding in each convolutional layer. This enables to super-resolve 2-D thermal images for nondestructive testing (NDT) with a substantially improved convergence rate compared to classic approaches. The performance of the proposed approach is evaluated on various deep unfolding and thresholding approaches. Furthermore, we explored how to increase the reconstruction quality and the computational performance. Thereby, it was found that the computing time for creating high-resolution images could be significantly reduced without decreasing the reconstruction quality by using pixel binning as a preprocessing step.