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AbstractThe planar flexible Koch fractal eddy current probe exhibits high consistency in detecting short cracks in all orientations. However, it is limited by a weak signal caused by the small number of coil turns. This paper utilizes multilayer flexible circuit board technology to address the issue. The probe is designed with a mutual inductance coil structure and is arranged on a 10-layer flexible circuit board. This board includes one layer for the excitation coil and nine layers for the pick-up coil. The probe’s performance was studied through C-scan experiments, which examined cracks of varying depths, widths, orientations, and lengths. The experimental results show that the probe can detect minimum crack sizes of 0.5 mm × 0.15 mm × 1 mm for 5083-aluminum alloy, 3 mm × 0.15 mm × 1 mm for GH4169 superalloy, and 1 mm × 0.15 mm × 1 mm for Q235 steel. The results demonstrate that the flexible fractal eddy current probe has significant advantages in detecting small cracks. [ABSTRACT FROM AUTHOR]

Maintaining the integrity of urban road pavements is vital for public safety, transportation efficiency, and economic stability. However, aging infrastructure and limited budgets make it challenging to detect subsurface defects that can lead to pavement collapses. Traditional inspection methods are often inadequate for identifying such underground anomalies. Ground Penetrating Radar (GPR), especially dual-polarized array systems, offers a non-destructive, high-resolution solution for subsurface inspection. Despite its potential, effectively detecting and analyzing areas at risk of collapse in urban pavements remains a challenge. This study employed a dual-polarized array GPR system to inspect road pavements in London. The research involved comprehensive field testing, including data acquisition, signal processing, calibration, background noise removal, and 3D migration for enhanced imaging. Additionally, Short-Fourier Transform Spectrum (SFTS) analysis was applied to detect moisture-related anomalies. The results show that dual-polarized GPR systems effectively detect subsurface issues like voids, cracks, and moisture-induced weaknesses. The ability to capture data in multiple polarizations improves resolution and depth, enabling the identification of collapse-prone areas, particularly in regions with moisture infiltration. This study demonstrates the practical value of dual-polarized GPR technology in urban pavement inspection, offering a reliable tool for early detection of subsurface defects and contributing to the longevity and safety of road infrastructure. [ABSTRACT FROM AUTHOR]

Localizing security-relevant hard blocks on modern System-on-Chips for physical attacks, such as side-channel analysis and fault attacks, has become increasingly time-consuming due to ever-increasing chip-area and -complexity. While this development increases the effort and reverse engineering cost, it is not sufficient to withstand resolute attackers. This paper explores the application of camera-based lock-in thermography, a nondestructive testing method, for identifying and localizing security hard blocks on integrated circuits. We use a synchronous signal to periodically activate security-related functions in the firmware, which causes periodic temperature changes in the activated die areas that we detect and localize via an infrared camera. Using this method, we demonstrate the precise detection and localization of security-related hard blocks at the die level on a modern SoC. [ABSTRACT FROM AUTHOR]

Electrical resistance tomography (ERT) is gaining recognition as an effective, affordable, and nondestructive tool for monitoring and imaging concrete structures. This paper discusses ERT's applications, including crack detection, moisture ingress monitoring, steel reinforcement assessment, and chloride level profiling within concrete. Recent advancements, such as time-lapse ERT and artificial intelligence (AI) integration, have enhanced image resolution and provided detailed data for infrastructure monitoring. However, challenges remain regarding the need for better spatial resolution, concrete-compatible electrodes, and integration with other nondestructive testing techniques. Addressing these issues will expand the applicability and reliability of the current ERT, making it an invaluable tool for infrastructure maintenance and monitoring. [ABSTRACT FROM AUTHOR]

A novel image-reconstruction method is proposed for the processing of data acquired at random spatial positions. The images are reconstructed and updated in real time concurrently with the measurements to produce an evolving image, the quality of which is continuously improving and converging as the number of data points increases with the stream of additional measurements. It is shown that the images converge to those obtained with data acquired on a uniformly sampled surface, where the sampling density satisfies the Nyquist limit. The image reconstruction employs a new formulation of the method of scattered power mapping (SPM), which first maps the data into a three-dimensional (3D) preliminary image of the target on a uniform spatial grid, followed by fast Fourier space image deconvolution that provides the high-quality 3D image. [ABSTRACT FROM AUTHOR]

This paper presents a study to accurately evaluate defects in concrete decks using ultrasonic pulse-echo signals. A reinforced concrete deck with void defects was designed and evaluated for validation, and a commercial ultrasonic pulse-echo (UPE) device was used to obtain the 2D images of the void defect inside the deck. The UPE image is based on the ultrasonic shear-wave test method and an extended synthetic aperture focusing technique (SAFT). To enhance the accuracy of the defect location in the SAFT imaging, the recorded A-scan data from UPE was analyzed using an advanced denoising approach and defect echo peak extraction, which are based on empirical modal decomposition, Hurst exponent characterization, and Hilbert envelope estimation. The results demonstrated that the location and depth of the void defect in the deck can be accurately assessed by using the developed approach. The new method provides quantitative information of the anomalies inside the deck, which can be used to calibrate the qualitative images of UPC devices with the SAFT. [ABSTRACT FROM AUTHOR]

Maintaining the integrity of urban road pavements is vital for public safety, transportation efficiency, and economic stability. However, aging infrastructure and limited budgets make it challenging to detect subsurface defects that can lead to pavement collapses. Traditional inspection methods are often inadequate for identifying such underground anomalies. Ground Penetrating Radar (GPR), especially dual-polarized array systems, offers a non-destructive, high-resolution solution for subsurface inspection. Despite its potential, effectively detecting and analyzing areas at risk of collapse in urban pavements remains a challenge. This study employed a dual-polarized array GPR system to inspect road pavements in London. The research involved comprehensive field testing, including data acquisition, signal processing, calibration, background noise removal, and 3D migration for enhanced imaging. Additionally, Short-Fourier Transform Spectrum (SFTS) analysis was applied to detect moisture-related anomalies. The results show that dual-polarized GPR systems effectively detect subsurface issues like voids, cracks, and moisture-induced weaknesses. The ability to capture data in multiple polarizations improves resolution and depth, enabling the identification of collapse-prone areas, particularly in regions with moisture infiltration. This study demonstrates the practical value of dual-polarized GPR technology in urban pavement inspection, offering a reliable tool for early detection of subsurface defects and contributing to the longevity and safety of road infrastructure.

This paper presents an air-coupled impact echo (IE) technique that relies on the phase spectrum of the collected data to find the frequencies corresponding to the reflections from delaminations. The proposed technique takes advantage of the fact that the IE compression wave is not a propagating wave, but it is the 1st order symmetrical (S1) mode Lamb wave at zero group velocity (S1-ZGV). Therefore, it searches the phase spectra of the data collected by multiple sensors to locate the frequency corresponding to the lowest phase difference. As a result, the technique reduces the effect of propagating waves, including the direct acoustic wave and ambient noise. It is named the Constant Phase IE (CPIE). The performance of the CPIE is experimentally compared with the regular amplitude spectrum-based IE technique and two other multisensor IE techniques. The CPIE shows a performance advantage, especially in a noisy environment. [ABSTRACT FROM AUTHOR]

Although accurately estimating the early age compressive strength of concrete is essential for the timely removal of formwork and the advancement of construction processes, it is challenging to estimate it in cool, cold, hot, or unmanaged conditions. Various nondestructive testing methods, including recent IoT-based techniques, have been proposed to determine the compressive strength of concrete. This study evaluates the maturity method using the wireless thermocouple sensor in assessing the early age compressive strength of concrete slabs, particularly those not subjected to watering and protection in a cool environment below 20 °C. For this purpose, wire and wireless thermocouple sensors were installed in reinforced concrete (RC) slabs, whereas wire thermocouple sensors were installed in concrete cylinders. In addition, the compressive strengths of standard-cured cylinders, field-cured cylinders, and core samples extracted from the RC slab were measured. On day 7, the maturity index (M) values for the field-cured cylinders were 7% lower than those of the standard-cured cylinders, and the M values for the RC slabs with wire and wireless sensors were 6% lower. The compressive strengths of the field-cured cylinders and core samples extracted from the RC slabs were 19% and 14% lower than those of the standard-cured cylinders, respectively. Thus, while the difference in M values was 6–7%, the difference in compressive strength was significantly higher, at 14–19%. In a cool environment without watering or protection, the difference in strength can be even greater. Consequently, a commercial IoT-based thermocouple sensor can replace conventional wire sensors and adopt to estimate early age compressive strength of concrete in unmanaged curing condition. [ABSTRACT FROM AUTHOR]

Pitting corrosion, a localized form of corrosion leading to cavities and structural failure in metallic materials, requires early detection for effective mitigation. While ultrasonic inspection techniques can readily detect uniform wall thinning, they often struggle to identify pitting corrosion. This study proposes a time-lapse ultrasound inspection method to detect early-stage pitting using pulse-echo sensors. By recording multiple ultrasonic traces over time, 2-D timelapse images of ultrasonic reflectivity can be generated and fed into a trained neural network for pitting diagnostics. In general, training a machine-learning model requires a large training dataset. This work used data from a drilling experiment to generate a suitable dataset. Dataset construction by random time-ordered combinations of ultrasonic measurements was conducted to create a diverse set of time-lapse image samples to generalize the resulting machine-learning model adequately. A classification neural network was trained to detect the presence of drilled holes, and a separate regression network was trained to estimate the hole depth. Based on drilling data from an independently acquired test dataset, results demonstrate a mean absolute error of 0.163 mm for hole depth estimations. All holes are successfully detected when 0.1 mm deeper than the defined pitting threshold of 0.5 mm. This suggests that the proposed method generalizes well and can be deployed to any similar acquisition system.

Acoustic emission (AE) is one of the most effective nondestructive testing (NDT) techniques for the identification and characterization of stress waves originated at the uprising of acoustic-related defects (e.g., cracks). To this end, the estimation of the time of arrival (ToA) is crucial. In this work, a novel processing flow which shifts the identification process from the time to the time-frequency domain via wavelet transform (WT) is proposed, allowing to better capture transient behaviors typical of the originated AE signals. More specifically, both the continuous and the discrete WT alternatives have been explored to find the best compromise between time-frequency resolution and computational complexity in view of extreme edge deployments. Furthermore, the event-driven capabilities of neuromorphic architectures (and spiking neural networks (SNNs) in particular) in processing spiky and sparse temporal information are exploited to retrieve ToA in a beyond state-of-the-art power-efficient manner and negligible loss of performance with respect to standard models. Therefore, we aim at combining the superior performances in ToA identification enabled by the WT operator with the unique energy saving disclosed by spiking hardware and software. Experimental tests executed on a metallic plate structure demonstrated that WT combined with SNN can achieve high precision (median values less than 5 cm) in ToA estimation and AE source localization even in the presence of relevant noise (signal-to-noise ratio down to 2 dB), while its deployment on dedicated neuromorphic architectures can reduce by six orders of magnitude the power expenditure per inference when compared to standard convolutional architectures.

Electrical resistance tomography (ERT) is gaining recognition as an effective, affordable, and nondestructive tool for monitoring and imaging concrete structures. This paper discusses ERT’s applications, including crack detection, moisture ingress monitoring, steel reinforcement assessment, and chloride level profiling within concrete. Recent advancements, such as time-lapse ERT and artificial intelligence (AI) integration, have enhanced image resolution and provided detailed data for infrastructure monitoring. However, challenges remain regarding the need for better spatial resolution, concrete-compatible electrodes, and integration with other nondestructive testing techniques. Addressing these issues will expand the applicability and reliability of the current ERT, making it an invaluable tool for infrastructure maintenance and monitoring.

A 3D numerical model of eddy current nondestructive testing (ECNDT) based on the kernel truncated (KT), adaptive cross approximation (ACA) and singular value decomposition (SVD) algorithms is proposed. It is the first time to apply the KT-ACA-SVD algorithm to accelerate the boundary element method (BEM)‐based ECNDT model. The Stratton-Chu formulation is selected, which has no low frequency breakdown issue, as the boundary integral equation. The equivalent surface electric and magnetic field currents, and normal component of the magnetic field are expended by the Rao-Wilton-Glisson (RWG) vector basis functions, and pulse basis functions, respectively. The Galerkin’s method is chosen as the testing method, then the impedance matrix can be achieved. With the help of octree structure, the impedance matrix can be partitioned into diagonal, near and far block interactions which is decided by the distances between them. The diagonal and near block interactions are computed directly by the full matrix method, while the far block interactions are compressed by the KT-ACA-SVD algorithm. Finally, several nondestructive testing (NDT) tests are conducted to compare the impedance variations predicted by the proposed model with the ones achieved by other methods, including analytical, semi-analytical methods, and the experiment. The results demonstrate both the accuracy and efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

Evaluation of pavement deflection bowl using nondestructive testing (NDT) is an ideal method for assessing the structural capacity of individual layers of a pavement system. However, the cost of modern NDT equipment and the complexity involved in the back-calculation process makes it difficult for the road agencies to back-calculate individual layer properties and evaluate the in situ condition of existing pavements. This study developed low-cost equipment (LCE) for evaluating the pavement deflection bowl under static loading conditions and to correlate the results with the deflection bowl observed under dynamic loading on same pavement combinations. The study also analyzed the deflection bowl parameters through an understanding of the load distribution evaluated using linear elastic software. The moduli of the individual pavement layer were back-calculated using back-calculation software and correlated with the deflection bowl parameters. The study concluded that the deflection bowl under dynamic loading was only 0.65–0.75 times that of the deflection bowl under static loading. The difference of deflection measured at radial distances of 0 and 450 mm of the deflection bowl correlated well with the base-layer modulus, and the difference of deflection measured at radial distances of 300 and 600 mm correlated well with the subbase-layer modulus. Based on the deflection bowl data collected from nine test sections, it was found that emulsion-treated base layers exhibited 40%–60% higher moduli values than the conventional base layers during the monsoon season. Low-volume roads with dense surface mixes 30 mm thick exhibited significantly improved structural performance compared with that of low-volume roads with open-graded surface mixes 20 mm thick with a 6-mm seal coat. [ABSTRACT FROM AUTHOR]

This work presents how significantly the proper selection of the magnetization method can improve almost all parameters of the magnetic method and affect the effectiveness of the evaluation of reinforced concrete (RC) structures. Three magnetization methods are considered in this paper: opposite pole magnetization (typical solution), same pole magnetization, and (as a reference point) no magnetization. The experiments are carried out in a three-dimensional (XYZ) space. Measurements along each of the axes are discussed in a separate section. The results show that the appropriate selection of the magnetization method can affect noise reduction, signal strength, and the separation of measurements carried out on different samples. This paper also discusses the situations when the magnetization may change the shape, cause deformations of waveforms, affect the area testing, and be used to significantly increase the efficiency of simultaneous evaluation of three basic parameters of RC structure. Experiments and simulations have proven that properly applied magnetization may strongly affect the evaluation's effectiveness, making the magnetic method one of the most promising techniques in testing RC constructions. [ABSTRACT FROM AUTHOR]

This paper presents the development of the lock-in thermography system with an additional cooling system. System feasibility is tested by investigating a square-shaped glass fiber-reinforced polymer (GFRP) with artificially made outer flaws. The influence of heating mode and sinusoidal excitation period on the defect detectability is considered. Thus, the experiment is split into two modes: the sample is solely heated in the first mode or simultaneously heated and cooled in the second. In each mode, the temperature measurement is performed first with a shorter excitation signal period and second with a longer one. The signal-to-noise ratio (SNR) is used to assess defect detection quantitatively. The comparative analysis shows that employing a mixed heating–cooling mode improves the SNR compared to the conventional heating mode. The further enhancement of the SNR is obtained by extending the excitation period. The combination of simultaneous heating and cooling with longer periods of the excitation signal allows for the best SNR values for the most detected defects. [ABSTRACT FROM AUTHOR]

Based on the visible and near-infrared reflectance spectroscopy technique, stacked supervised autoencoders (SSAE) in deep learning were used to model the anthocyanin content of blueberry pomace. First, preprocessing and feature screening for spectral data were performed. With the minimum value of prediction set root mean square error (RMSEP) of the preset SSAE model as the standard, 178 characteristic wavelengths were selected. The absorbance of the selected characteristic wavelength was used as the input to the SSAE model. The anthocyanin content of blueberry pomace was used as the output. By exploring the activation parameters, node number, training times and learning rate of the SSAE model, the optimal parameters of SSAE were obtained, namely, the activation function of rule, the structure of 178-60-5-1, the training times of 70, and the learning rate of 0.01. The training set root mean square error (RMSEC), prediction set root mean square error (RMSEP), and prediction set correlation coefficient (Rp) were selected as the evaluation criteria. The RMSEC, RMSEP, and Rp of the established model were 1.0500, 0.3835, and 0.9042, respectively. Compared with the classic regression prediction model extreme learning machine (ELM), least squares support vector regression (LSSVR) and partial least squares regression (PLSR) algorithm, the prediction accuracy of the SSAE model was higher. Therefore, the combination of the SSAE model with visible and near-infrared reflectance spectroscopy proved to be effective in predicting anthocyanin content of blueberry pomace.

This paper presents a study to accurately evaluate defects in concrete decks using ultrasonic pulse-echo signals. A reinforced concrete deck with void defects was designed and evaluated for validation, and a commercial ultrasonic pulse-echo (UPE) device was used to obtain the 2D images of the void defect inside the deck. The UPE image is based on the ultrasonic shear-wave test method and an extended synthetic aperture focusing technique (SAFT). To enhance the accuracy of the defect location in the SAFT imaging, the recorded A-scan data from UPE was analyzed using an advanced denoising approach and defect echo peak extraction, which are based on empirical modal decomposition, Hurst exponent characterization, and Hilbert envelope estimation. The results demonstrated that the location and depth of the void defect in the deck can be accurately assessed by using the developed approach. The new method provides quantitative information of the anomalies inside the deck, which can be used to calibrate the qualitative images of UPC devices with the SAFT.

Machine Learning bears great potential for data‐driven solutions in the field of nondestructive testing (NDT) of concrete structures. The analysis of the data collected with NDT methods, such as ultrasonics, impact‐echo and ground penetrating radar, can be complex and requires experience. The expected benefit of Machine Learning applications in this context goes beyond the increase of efficiency obtained by automating the analysis process. While traditional analysis approaches are usually solely based on key features according to the basic principles, Machine Learning algorithms can consider the full data content and reveal hidden correlations. For an organized approach to Machine Learning on NDT data, an analysis tool has been developed. The tool provides a graphical user interface to manage and label training/test data and interactively define the Deep Neural Network architecture. In particular, Convolutional Neural Networks, as proven successful in various image recognition tasks, are implemented. The Machine Learning concepts are demonstrated in show cases, comprising ultrasonic as well as impact‐echo applications. In particular, the relevance of a targeted preprocessing is addressed, comparing the effectiveness of time‐, frequency‐ and joint‐time‐frequency‐representations. [ABSTRACT FROM AUTHOR]

With the increasing power and speed of laser welding, in-process monitoring has become even more crucial to ensure process stability and weld quality. Due to its low cost and installation flexibility, acoustic process monitoring is a promising method and has demonstrated its effectiveness. Although its feasibility has been the focus of existing studies, the temporal resolution of acoustic emissions (AE) has not yet been addressed despite its utmost importance for realizing real-time systems. Aiming to provide a benchmark for further development, this study investigates the relationship between duration and informativeness of AE signals during high-power ( 3.5   k W) and high-speed (12 m / min) laser beam butt welding. Specifically, the informativeness of AE signals is evaluated based on the accuracy of detecting and quantifying joint gaps for various time windows of signals, yielding numerical comparison. The obtained results show that signals can be shortened up to a certain point without sacrificing their informativeness, encouraging the optimization of the signal duration. Our results also suggest that large gaps (> 0.3 m m) induce unique signal characteristics in AE, which are clearly identifiable from 1 m s signal segments, equivalent to 0.2 m m weld seam. [ABSTRACT FROM AUTHOR]

Reinforced concrete (RC) parts, which are exposed to high temperatures for a certain period, undergo physical-chemical changes that deteriorate their mechanical properties. To this end, this study investigates the efficiency of using ground-penetrating radar (GPR) as a non-destructive testing (NDT) method in assessing the core compressive strength loss, elastic modulus decrease, and tensile strength loss of concrete under extreme temperatures. RC specimens are fabricated and exposed to elevated temperatures (300°C, 400°C, 500°C, 600°C, and 700°C). GPR measurements are performed to estimate the relative dielectric permittivity (RDP) of the specimens before fire exposure (BFE) and after fire exposure (AFE). The compressive strengths of the cores extracted from the specimens are obtained also for BFE and AFE cases. Attained RDP values are in conformance with the previous research results and contribute additional evidence to the published literature. As the temperature levels are increased, RDP and strength-related values of concrete specimens decrease. The strength-related parameters are more sensitive to elevated temperatures, whereas the change in the RDP values is rather gradual. Through multiple correlations within 95% confidence intervals, core compressive strength loss, elastic modulus decrease, and tensile strength loss are, individually, estimated as a function of relative RDP change and applied temperature levels. Empirical formulas are obtained with high coefficients of correlation. The relative effects of RDP change and temperature are more pronounced in elastic modulus decrease and tensile strength loss compared to compressive strength loss. Overall, the outcomes of this paper confirm the viability of using the GPR technique for concrete dielectric characterisation in estimating the strength-related properties of concrete exposed to high temperatures, which might reduce the need for destructive testing. [ABSTRACT FROM AUTHOR]

The S-band electron linear accelerator developed for nondestructive testing is primarily used to inspect pressure vessels, petrochemical parts, and other industrial components. The X-band electron linear accelerator has been developed in RIAMB, owing to its advantageous flexibility and light weight and can be used for on-site testing. The integral solution of the irradiation electron linear accelerator system provided by RIAMB includes an accelerator, transmission line, production management system, automatic storage and retrieval system, and workshop. The electrocurtain accelerator is currently being developed in RIAMB. At present, it includes two models, with maximum accelerating voltages of 200 kV and 150 kV, respectively. The ion beam surface processing technology includes ion implantation surface modifications, magnetic filtration multi-arc ion coatings, and ion beam assisted deposition coatings. It forms a corrosion-resistant, friction-resistant, anti-icing, and friction-reducing film on the surface of parts such as bearings and pitot tubes, which are primarily used in the aerospace field.

This paper presents a study of the feasibility of using in-line inspection (ILI) techniques with long-range communication-capable robotic systems deployed with advanced inspection sensors in natural gas distribution pipelines, which are rare in the literature. The study involved selecting appropriate antennas and determining the appropriate communication frequency for an ILI robot operating on Istanbul 12″ and 16″ steel pipelines. The paper identifies the frequency windows with low losses, presents received signal strength indicator (RSSI) and signal-to-noise ratio (SNR) information for various scenarios, and evaluates the impact of T-junctions, which are known to be the worst components in terms of communication. To utilize the pipeline as a waveguide, low-attenuation-frequency windows were determined, which improved communication by a factor of 500 compared to aerial communication. The results of laboratory tests on a 50 m pipeline and real-world tests on a 2.4 km pipeline indicate that long-distance communication and video transmission are possible at frequencies of around 917 MHz with low-gain antennas. The study also assessed the impact of the early diagnosis of anomalies without incidents on the environment, achievable with ILI robots using long-range wireless communication. [ABSTRACT FROM AUTHOR]

Although accurately estimating the early age compressive strength of concrete is essential for the timely removal of formwork and the advancement of construction processes, it is challenging to estimate it in cool, cold, hot, or unmanaged conditions. Various nondestructive testing methods, including recent IoT-based techniques, have been proposed to determine the compressive strength of concrete. This study evaluates the maturity method using the wireless thermocouple sensor in assessing the early age compressive strength of concrete slabs, particularly those not subjected to watering and protection in a cool environment below 20 °C. For this purpose, wire and wireless thermocouple sensors were installed in reinforced concrete (RC) slabs, whereas wire thermocouple sensors were installed in concrete cylinders. In addition, the compressive strengths of standard-cured cylinders, field-cured cylinders, and core samples extracted from the RC slab were measured. On day 7, the maturity index (M) values for the field-cured cylinders were 7% lower than those of the standard-cured cylinders, and the M values for the RC slabs with wire and wireless sensors were 6% lower. The compressive strengths of the field-cured cylinders and core samples extracted from the RC slabs were 19% and 14% lower than those of the standard-cured cylinders, respectively. Thus, while the difference in M values was 6–7%, the difference in compressive strength was significantly higher, at 14–19%. In a cool environment without watering or protection, the difference in strength can be even greater. Consequently, a commercial IoT-based thermocouple sensor can replace conventional wire sensors and adopt to estimate early age compressive strength of concrete in unmanaged curing condition.

Investigations into the dielectric properties of concrete form the basis for concrete quality testing and condition monitoring of internal structures when using ground penetrating radar (GPR). The formula used to describe the quantitative relationship between the dielectric properties of composite materials and the dielectric constant and volumetric ratio of each component is known as a dielectric model. In this study, a new dielectric model was deduced from circuit theory on the basis of structural abstraction to calculate the dielectric permittivities of concrete. To validate the applicability and accuracy of the proposed model, because concrete consists of matrix (cement mortar), aggregates (limestone), and air, the dielectric traits of concrete and its components were acquired using an open-ended coaxial probe. Compared with the Brown model, complex refractive index method (CRIM) model, Looyenga model, and Rayleigh model (i.e., the classical models), the results showed that among overall predictions of concrete dielectric constants, the proposed model has the greatest accuracy, because the theoretical results matched the test results more closely. This indicates that the proposed model can better interpret the dielectric properties of concrete, which lays the groundwork for future research into the application of GPR to the nondestructive testing and evaluation of concrete pavement. [ABSTRACT FROM AUTHOR]

This paper presents the results of experiments using the eddy current system designated for nondestructive inspection of carbon fiber-reinforced composites. For this purpose, the eddy current testing system with a differential transducer with two pairs of excitation coils oriented perpendicularly and a central pick-up coil was utilized. The transducer measures the magnetic flux difference flowing through the pick-up coil. The transducer of this design has already been successfully utilized to inspect isotropic metal structures. However, the anisotropy of the composites and their lower conductivity compared to metal components made the transducer parameters adjustment essential. Thus, various excitation frequencies were considered and investigated. The system was evaluated using a sample made of orthogonally woven carbon fiber-reinforced composites with two artificial flaws (the notches with a maximum relative depth of 30% and 70%, respectively, thickness of 0.4 mm, and a length of 5 mm). The main goal was to find a configuration suitable for detecting hidden flaws in such materials. [ABSTRACT FROM AUTHOR]

Wireless monitoring of displacement between two points is demonstrated with an interrogation range (${D}_{\boldsymbol {m}}$) which is dramatically larger compared with previous configurations based on near-field coupling. The sensing system comprises a transmitting antenna, a nested split-ring resonator (NSRR) sensor functioning as a tag, and a probe, called a “sniffer coil.” In classical configuration, the NSRR and the antenna constitute an electromagnetically coupled system which exhibits high sensitivity and resolution but is only functional within the near-field of the antenna, which in practice is much smaller than one wavelength, which limits the capabilities of the sensor. In this work, the system is separated into the TX and RX channels, which enables the transmitter antenna to be placed at a location which is several wavelengths away from the sensor. To remove the effect of clutter that disrupted the sensing signal at higher ${D}_{\boldsymbol {m}}$ in previous works, the variation with frequency of the power level coupled to the sniffer coil is tracked instead of the variation in the scattering parameters with frequency. While extending ${D}_{\boldsymbol {m}}$ , the high sensitivity and resolution are retained by the coupling between the NSRR and the sniffer coil. Wireless sensing of displacement is essential for several application areas, most specifically, structural health monitoring (SHM). The fact that the sensing system can work in far-field and that the NSRR is passive allows for remote and nondestructive testing (NDT) of damaged structures and buildings. [ABSTRACT FROM AUTHOR]

With the increasing power and speed of laser welding, in-process monitoring has become even more crucial to ensure process stability and weld quality. Due to its low cost and installation flexibility, acoustic process monitoring is a promising method and has demonstrated its effectiveness. Although its feasibility has been the focus of existing studies, the temporal resolution of acoustic emissions (AE) has not yet been addressed despite its utmost importance for realizing real-time systems. Aiming to provide a benchmark for further development, this study investigates the relationship between duration and informativeness of AE signals during high-power (3.5 kW) and high-speed (12 m/min) laser beam butt welding. Specifically, the informativeness of AE signals is evaluated based on the accuracy of detecting and quantifying joint gaps for various time windows of signals, yielding numerical comparison. The obtained results show that signals can be shortened up to a certain point without sacrificing their informativeness, encouraging the optimization of the signal duration. Our results also suggest that large gaps (>0.3mm) induce unique signal characteristics in AE, which are clearly identifiable from 1 ms signal segments, equivalent to 0.2mm weld seam.

Model assisted probability of detection (MAPoD) is crucial for quantifying the inspection capability of a nondestructive testing (NDT) system which uses the coil or probe to sense the size and location of the cracks. Unfortunately, it may be computationally intensive for the simulation models. To improve the efficiency of the MAPoD, in this article, an efficient 3D eddy current nondestructive evaluation (ECNDE) forward solver is proposed to make estimations for PoD study. It is the first time that singular value decomposition (SVD) is used as the recompression technique to improve the overall performance of the adaptive cross approximation (ACA) algorithm-based boundary element method (BEM) ECNDE forward solver for implementation of PoD. Both the robustness and efficiency of the proposed solver are demonstrated and testified by comparing the predicted impedance variations of the coil with analytical, semi-analytical and experimental benchmarks. Calculation of PoD curves assisted by the proposed simulation model is performed on a finite thickness plate with a rectangular surface flaw. The features, which are the maximum impedance variations of the coil for various flaw lengths, are obtained entirely by the proposed model with selection of the liftoff distance as the uncertain parameter in a Gaussian distribution. The results show that the proposed ACA-SVD based BEM fast ECNDE forward solver is an excellent simulation model to make estimations for MAPoD study. [ABSTRACT FROM AUTHOR]

This article proposes a novel framework to characterize the morphological pattern of barely visible impact damage using machine learning. Initially, a sequence of image processing methods is introduced to extract the damage contour, which is then described by a Fourier descriptor-based filter. The uncertainty associated with the damage contour under the same impact energy level is then investigated. A variety of geometric features of the contour are extracted to develop an artificial intelligence model, which effectively groups the tested 100 samples impacted by 5 different impact energy levels with an accuracy of 96%. Predictive polynomial models are finally established to link the impact energy to the three selected features. It is found that the major axis length of the damage has the best prediction performance, with an R2 value up to 0.97. Additionally, impact damage caused by low energy exhibits higher uncertainty than that of high energy, indicating lower predictability. [ABSTRACT FROM AUTHOR]

U-shaped excitation structures are widely used to magnetize steel plates in magnetic flux leakage (MFL) testing. However, the edge effect leads to the difficulty of further increasing the magnetization near the defect as well as the leakage magnetic field (LMF). Therefore, this article proposes an MFL testing device with a pot-shaped excitation structure to concentrate on the main magnetic flux in the steel plate between the two magnetic poles for improving the LMF. First, the simulated comparison of the conventional U-shaped and the proposed pot-shaped structures is presented in this article. Then, a sensor array is applied to investigate the measured MFL signal components and the C-scan images of the pot-shaped structure. Finally, the experiments are conducted to verify the effectiveness of the simulations and investigate the effect of the relative position of the sensor. The results show that the peak-to-peak value of the MFL signal can increase to 1.93 times after using the pot-shaped structure. Besides, its signal is not affected by the steel plate width and remains the ability to characterize defect sizes and profiles. [ABSTRACT FROM AUTHOR]

There is a rapid trend in various industries to replace the metallic pipes by nonmetallic ones. This is due to the certain properties, such as high strength, lightweight, resilience to corrosion, and low cost of maintenance for nonmetallic pipes. Despite the abovementioned advantages, nonmetallic pipes are still affected by issues, such as erosion, defects, damages, cracks, holes, delamination, and changes in the thickness. These issues are typically caused due to the manufacturing process, type of carried fluid composition, and flow rate. If not examined well, these issues could lead to disastrous failures caused by leakages and bursting of the pipes. To prevent such major failures, it is extremely important to test the pipes periodically for an accurate estimation of their thickness profile. In this article, we propose a nondestructive testing (NDT) technique, based on near-field microwave holography, for identifying the fluid carried by a nonmetallic pipe and estimating the pipe’s thickness profile. Identifying the carried fluid helps improve the thickness profile estimation. The performance of the proposed techniques will be demonstrated via simulations and experiments. [ABSTRACT FROM AUTHOR]

• A state monitoring method for surface crack extension of welded structures based on the FMC-TFM technique is proposed. • The limitation of difficulty in quantitatively obtaining the internal evolution morphology of welded joints is solved. • An analytic function for fatigue crack evolution improves the accuracy of welded structural integrity assessment. In this study, the fatigue crack propagation behavior of aluminum alloy butt welds was innovatively investigated using a phased-array ultrasonic total-focus imaging nondestructive testing technique. The crack propagation path determined with the traction structural stress method was verified. The length and depth propagation pattern of the weld toe surface crack was determined based on the fatigue test and condition monitoring crack evolution data in full scale cyclic tensile loading model. The results showed that the weld toe surface crack evolves as a pattern of multiple small crack propagation fusing to converge a main crack and fail through the thickness. The condition-monitoring analysis method for surface fatigue crack propagation is proposed to improve the completeness of the fatigue crack propagation law. [ABSTRACT FROM AUTHOR]

• Utilize finite element analysis to investigate the infrared and terahertz non-destructive inspection principle for artworks. • Propose a new terahertz feature extraction algorithm, which converts time-step data to 2D images and enhances the noise robustness. • Implement an unsupervised fusion algorithm, which integrates the features obtained from both infrared and terahertz imaging. This study utilizes infrared thermography (IRT) and terahertz (THz) imaging techniques to conduct a non-invasive inspection of marqueteries. Specifically, two experimental investigations and image processing methods have been carried out, and their advantages and disadvantages have been highlighted. To cross-reference with the experimental results, a finite element simulation was performed for both IRT and THz, which validate the experimental outcomes and providing instructions to the experimental set-up and parameters. Then, an ad-hoc fusion imaging method has been applied to the experimental results to identify defects and features. Such a reverse engineering procedure integrating numerical and experimental images acquired at different wavelengths may be considered an interesting step forward for the optimization of cultural heritage nondestructive testing (NDT). [ABSTRACT FROM AUTHOR]

In this review, the potential failures and flaws associated with fused deposition modeling (FDM) or fused filament fabrication (FFF) 3D printing technology are highlighted. The focus of this article is on presenting the failures and flaws that are caused by the operational standpoints and which are based on the many years of experience with current and emerging materials and equipment for the 3D printing of polymers and composites using the FDM/FFF method. FDM or FFF 3D printing, which is also known as an additive manufacturing (AM) technique, is a material processing and fabrication method where the raw material, usually in the form of filaments, is added layer-by-layer to create a three-dimensional part from a computer designed model. As expected, there are many advantages in terms of material usage, fabrication time, the complexity of the part, and the ease of use in FDM/FFF, which are extensively discussed in many articles. However, to upgrade the application of this technology from public general usage and prototyping to large-scale production use, as well as to be certain about the integrity of the parts even in a prototype, the quality and structural properties of the products become a big concern. This study provides discussions and insights into the potential factors that can cause the failure of 3D printers when producing a part and presents the type and characteristics of potential flaws that can happen in the produced parts. Common defects posed by FDM printing have been discussed, and common nondestructive detection methods to identify these flaws both in-process and after the process is completed are discussed. The discussions on the failures and flaws in machines provides useful information on troubleshooting the process if they happen, and the review on the failures and flaws in parts helps researchers and operators learn about the causes and effects of the flaws in a practical way. [ABSTRACT FROM AUTHOR]

The traditional underwater alternating current filed measurement（ACFM） does not take into account the influence of shell material on the detection signal and each parameter of the equipment in the simulation design. In order to solve this problem, an ACFM simulation model was established, the size and shape of the magnetic core were optimized and the probe size was reduced. The effects of the conductivity and permeability of the nondestructive testing（NDT） probe housing material on the detection signal were simulated and analyzed. The most suitable shell material for deep water NDT is 304 stainless steel and the best current frequency, and the parameters of the NDT probe were optimized. The results show that when the conductivity of the shell material reaches 107 S/m, the characteristic signal detects by the magnetic sensor reverses and the frequency of the current more than 7 kHz leads to serious distortion of the detection signal. The permeability of shell material attenuates seriously to the amplitude of Magnetic induction intensity Bz in z direction and magnetic induction intensity Bx in x direction. The research results are beneficial to the development and design of NDT. Reset [ABSTRACT FROM AUTHOR]

In recent years, trees in European countries have been increasingly endangered by emerging infectious diseases (EIDs). In the United Kingdom (U.K.), this has been observed to affect whole woodlands and forests, threatening the existence of some types of trees. Although quarantine measures have been taken to limit the spreading of such diseases, this has not yet been effectively controlled leading to millions of trees affected by EIDs. Ground-penetrating radar (GPR) has proven effective in identifying critical features on diseased trees for detection of EIDs spread. However, the irregular shape of tree trunks and their complex internal structure represent real challenges for conventional GPR measurements and data processing methodologies. In this research, a dual-polarized GPR system is used to detect internal decay in tree trunks using novel signal processing methodologies. A polarimetric correlation filter based on Bragg scattering on a 3-D Pauli feature vector and an arc-shaped Kirchhoff migration are discussed in detail. The proposed polarimetric correlation filter is utilized to enhance the signal-to-noise ratio (SNR) of B-scans due to bark and tree trunk high-loss properties of tree trunks. Meanwhile, an arc-shaped Kirchhoff migration algorithm is performed to counteract the influence of the bark irregularity. The proposed data processing framework is successfully validated with measurements on a real tree trunk, where cross sections were subsequently cut for comparison purposes. Outcomes from the proposed methodology demonstrate a high consistency with the features observed on the tree trunk cross sections, indicating the reliability of the proposed detection scheme for assessing tree-decay associated with EIDs. [ABSTRACT FROM AUTHOR]

In the field of ultrasonic nondestructive testing (NDT), the total focusing method (TFM) and its derivatives are now commercially available on portable devices and are getting more popular within the NDT community. However, its implementation requires the collection of a very large amount of data with the full matrix capture (FMC) as the worst case scenario. Analyzing all the data also requires significant processing power, and consequently, there is an interest in: 1) reducing the required storage capacity used by imaging algorithms, such as delay-and-sum (DAS) imaging and 2) allowing the transmission and postprocessing of inspection data remotely. In this study, a different implementation of the TFM algorithm is used based on the vector coherence factor (VCF) that is used as an image itself. This method, also generally known as phase coherence imaging, presents certain advantages, such as a better sensitivity to diffracting geometries, consistency of defect restitution among different views, and an amplitude-free behavior as only the instantaneous phase of the signal is considered. Some drawbacks of this method must also be mentioned, including the fact that it poorly reproduces planar reflectors and presents a lower signal-to-noise ratio (SNR) than amplitude-based methods. However, previous studies showed that it can be used as a reliable tool for crack-like defect sizing. Thus, a lightweight acquisition process is proposed through single-bit digitization of the signal, followed by a phase retrieval method based on the rising and falling edge locations, allowing to feed the phase coherence imaging algorithm. Simulated and experimental tests were first performed in this study on several side-drilled holes (SDHs) in a stainless steel block and then extended to an experimental study on angled notches in a 19.05-mm (${{3}}/{{4}}''$)-thick steel sample plate through multiview imaging. Results obtained using the array performance indicator (API) and the contrast-to-noise ratio (CNR) as quantitative evaluation parameters showed that the proposed lightweight acquisition process, which relies on binary signals, allows a reduction of the data throughput of up to 47 times. This throughput reduction is achieved while still presenting very similar results to phase coherence imaging based on the instantaneous phase derived from the Hilbert transform of the full waveform. In an era of increasing wireless network speed and cloud computing, these results allow considering interesting perspectives for the reduction of inspection hardware costs and remote postprocessing. [ABSTRACT FROM AUTHOR]

X-ray imaging is proven effective in the visualization of defects inside the Aluminum Conductor Composite Core (ACCC) wires. Although object detection pipelines have been extensively considered in the nondestructive testing tasks, the difficulty in obtaining defect samples has become the main obstacle to the application of such methods in the task of automatic defect detection for ACCC wires X-ray images. In this paper, we conducted a new semi-supervised approach based on anomaly detection. Different from the commonly used supervised methods, the proposed method requires only samples without defects for the learning process, therefore we are no longer limited by the insufficient and unbalanced defect samples. Experimental results show that the accuracy of the proposed method is up to 0.761, which proves the effectiveness of the method in the automatic defect detection of ACCC wires X-ray images.

The formulation of an innovative concrete that meets the requirements of a self-compacting concrete (SCC), with acceptable performance in terms of rheology in the fresh state; good fluidity, ease of placing, without segregation as well as good mechanical strength and durability at hardened state has become of great research interest for the last decades. Numerous studies have shown the favorable effects of limestone fillers on the SCC properties. This study aims at investigating the effect of inert mineral addition of limestone fillers with dosages of 10% and 20% grinded to different fine nesses 2000, 3000 and 4000 cm²/g on the physico-mechanical properties of a fresh self-placing concrete using slump, the L-box and the sieve stability tests. Also, the means of destructive and non-destructive tests (NDT) methods to the assessment of the mechanical performances of SCC at hardened state were used. The use of experiment factorial design method allows us to have behavior laws to predict the mechanical strength response when combined with (NDT) according to a numerical model in such study. Hence, a numerical modeling of mechanical response could be derived by such statistical analysis in regards to the effects of factors and their interaction. The results obtained showed that the incorporation of limestone filler in the composition of the SCC improves the fluidity with limited segregation, as well as the good mechanical performances (resistance to compression and flexion). The numerical modeling of the predicted compressive strength response, in particular at the age of 28 days, is judged to be with an acceptable determined coefficient R² equal to 0.994. [ABSTRACT FROM AUTHOR]