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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.
When dealing in ultrasonic testing with inhomogeneous material structure
data interpretation can be rather difficult. This is especially the case when using
anisotropic dissimilar welds made from austenitic steel or nickel based alloys, which are
currently used for modern power plant concepts. For better understanding of the
complex interaction between the sound field and the component under test, the
visualization of sound propagation in solids is a substantial task to increase the
probability of detection of relevant defects. However, there exist only a small number of
appropriate techniques published today, such as scanning laser interferometer,
piezoelectric and optical approaches in case of transparent solids. In this work we
present an electrodynamic technique providing a simple use and a high signal to noise
ratio. By detecting the grazing beam with an electrodynamic probe with a size smaller
than 10 mm, we measured the particle displacement as a function of time with a spatial
resolution in the order of 1 mm. Adapting the electrodynamic probe and its coil
alignment allows for measuring the displacement components in all three dimensions.
This comprises the detection of the horizontal and vertical particle displacement with
respect to the surface and thus also the transformation from longitudinal waves into
transversal waves and vice versa is possible. A SNR of higher than 36 dB could be
achieved within ferromagnetic and high conductive chrome steel when using a
transversal wave generated by an angled beam transducer. We report on measurements
of the sound field in complex weld joints. One example shows a 10 mm thick narrowgap
weld joining a nickel alloy with a chrome steel yielding a substantial anisotropy of
the weld structure. The test system enables us to visualize the wave propagation within
the weld and indicates the reflection scenario and the energy losses due to both the
anisotropic structure and material defects.
Surface wave velocities over the sonic frequency range (<20 KHz) were measured on concrete specimens undergoing various cycles of loading and unloading. Acoustic Emission test (AE) was conducted simultaneously to monitor the microcracking activities. The sonic surface wave velocity was found to be highly stress-dependent. The observed changes in surface wavespeed are repeatable and follow a particular trend. By measuring the wave velocities in both loading and unloading phases, the effects of stress and stress-induced damages could be distinguished. The observed trend could be explained by a combination of acoustoelasticity and microcracking theories.
The accuracy and precision of low-frequency (center frequency of approximately 55 kHz) ultrasonic testing for detection and characterization of delamination in concrete bridge decks were evaluated. A multiprobe ultrasonic testing system (with horizontally polarized shear-wave transducers) was used to detect built-in delamination defects of various size, depth, and severity (i.e., thickness) in a test specimen—a 6.1 m × 2.4 m × 216 mm (20 ft × 8 ft × 8.5 in.) reinforced concrete slab-built to simulate a concrete bridge deck. The collected data sets were reconstructed applying synthetic aperture focusing technique (SAFT). The reconstructed measurement results were then used to assess the condition of the concrete slab at individual points [point-by-point data collection and two-dimensional (2D) reconstruction] as well as along lines, where data were collected at smaller steps and reconstructed in a three-dimensional (3D) format. The local-phase information was also calculated, superimposed on the reconstructed images and used as complementary information in condition assessment. The precision and accuracy of condition assessments were evaluated. The results indicated that, using the multiprobe ultrasonic array, delamination defects as small as 30 cm² (1 ft²) could be reliably detected. Deep delaminations [i.e., those deeper than 150 mm (6 in.)] were directly detected and characterized, whereas shallow delaminations [shallower than 65 mm (2.5 in.)] were detected only indirectly. The precision of the measurements was demonstrated by comparing repeated measurements at several test points. Similar measurement results obtained on a delaminated portion of a real bridge deck support the conclusions of the validation study.
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.
Adhesively bonded composite joints can develop voids and porosity during fabrication, leading to stress concentration and a reduced load-carrying capacity. Hence, adhesive porosity analysis during the fabrication is crucial to ensure the required quality and reliability. Ultrasonic-guided wave (UGW)-based techniques without advanced signal processing often provide low-resolution imaging and can be ineffective for detecting small-size defects. This article proposes a damage imaging process for adhesive porosity analysis of bonded composite plates using UGWs measured by scanning laser Doppler vibrometer (LDV). To implement this approach, a piezoelectric transducer is mounted on the composite joint specimen to generate UGWs, which are measured over a densely sampled area. The signals obtained from the scan are processed using the proposed signal processing in different domains. Through the utilization of filter banks in frequency and wavenumber domains, along with the root-mean-square calculation of filtered signals, damage images of the adhesive region are obtained. It has been observed that different filters provide information related to different void sizes. Combining all the images reconstructed by filters, a final image is obtained which contains damages of various sizes. The images obtained by the proposed method are verified by radiography results and the porosity analysis is presented. The results indicate that the proposed methodology can detect the pores with the smallest detectable pore area of 2.41 mm^2, corresponding to a radius of 0.88 mm, with an overall tendency to overestimate the pore size by an average of 11%.