Search Results (930)

Agricultural nondestructive testing technology is pivotal in safeguarding food quality assurance, safety monitoring, and supply chain transparency. While conventional optical methods such as near-infrared spectroscopy and hyperspectral imaging demonstrate proficiency in surface composition analysis, their constrained penetration depth and environmental sensitivity limit effectiveness in dynamic agricultural inspections. This review highlights the transformative potential of microwave technologies, systematically examining their operational principles, current implementations, and developmental trajectories for agricultural quality control. Microwave technology leverages dielectric response mechanisms to overcome traditional limitations, such as low-frequency penetration for grain silo moisture testing and high-frequency multi-parameter analysis, enabling simultaneous detection of moisture gradients, density variations, and foreign contaminants. Established applications span moisture quantification in cereal grains, oilseed crops, and plant tissues, while emerging implementations address storage condition monitoring, mycotoxin detection, and adulteration screening. The high-frequency branch of the microwave–millimeter wave systems enhances analytical precision through molecular resonance effects and sub-millimeter spatial resolution, achieving trace-level contaminant identification. Current challenges focus on three areas: excessive absorption of low-frequency microwaves by high-moisture agricultural products, significant path loss of microwave high-frequency signals in complex environments, and the lack of a standardized dielectric database. In the future, it is essential to develop low-cost, highly sensitive, and portable systems based on solid-state microelectronics and metamaterials, and to utilize IoT and 6G communications to enable dynamic monitoring. This review not only consolidates the state-of-the-art but also identifies future innovation pathways, providing a roadmap for scalable deployment of next-generation agricultural NDT systems. Full article

Natural carbonate stones such as limestones and marbles are widely used in heritage and contemporary architecture, yet their durability is increasingly threatened by wildfire-related thermal stress. Since water transport plays a key role in stone deterioration, understanding how high temperatures affect hydric behavior is critical for conservation. This study investigates thirteen Portuguese carbonate lithotypes (including marbles, limestones, a travertine, and a breccia) exposed to temperatures of 300 °C and 600 °C. Capillary absorption and open porosity were measured, alongside Leeb hardness (HL) and ultrasonic pulse velocity (UPV), to evaluate their predictive capacity for post-fire moisture behavior. Results show that thermal exposure increases porosity and capillary uptake while reducing mechanical cohesion. Strong correlations between UPV and hydric parameters across temperature ranges highlight its reliability as a non-invasive diagnostic tool. HL performed well in compact stones but was less consistent in porous or heterogeneous lithologies. The findings support the use of NDT tests, like UPV and HL, for rapid post-fire assessments and emphasize the need for lithology-specific conservation strategies. Full article

This study comprehensively compares the performance of two non-destructive testing (NDT) techniques—active thermography (AT) and digital shearography (DS)—for identifying various damage types in composite structures. Three distinct composite specimens were inspected: a carbon-fiber-reinforced polymer (CFRP) plate with flat-bottom holes, an aluminum honeycomb core sandwich plate with a circular skin-core disbond, and a CFRP plate with two low-energy impacts damage. The research highlights the significant role of post-processing methods in enhancing damage detectability. For AT, algorithms such as fast Fourier transform (FFT) for temperature phase extraction and principal component thermography (PCT) for identifying significant temperature components were employed, generally making anomalies brighter and easier to locate and size. For DS, a novel band-pass filtering approach applied to phase maps, followed by summing the filtered maps, remarkably improved the visualization and precision of damage-induced anomalies by suppressing background noise. Qualitative image-based comparisons revealed that DS consistently demonstrated superior performance. The sum of DS filtered phase maps provided more detailed and precise information regarding damage location and size compared to both pulsed thermography (PT) and lock-in thermography (LT) temperature phase and amplitude. Notably, DS effectively identified shallow flat-bottom holes and subtle imperfections that AT struggled to clearly resolve, and it provided a more comprehensive representation of the impacts damage location and extent. This enhanced capability of DS is attributed to the novel phase map filtering approach, which significantly improves damage identification compared to the thermogram post-processing methods used for AT. Full article

Corrosion in maritime ballast tanks is a major driver of maintenance costs and operational risks for maritime assets. Inspections are hampered by complex geometries, hazardous conditions, and the limitations of conventional methods, particularly visual assessment, which struggles with subjectivity, accessibility, and early detection, especially under coatings. This paper critically examines these challenges and explores the potential of Light Detection and Ranging (LiDAR) and Hyperspectral Imaging (HSI) to form the basis of improved inspection approaches. We discuss LiDAR’s utility for accurate 3D mapping and providing a spatial framework and HSI’s potential for objective material identification and surface characterization based on spectral signatures along a wavelength range of 400-1000nm (visible and near infrared). Preliminary findings from laboratory tests are presented, demonstrating the basic feasibility of HSI for differentiating surface conditions (corrosion, coatings, bare metal) and relative coating thickness, alongside LiDAR’s capability for detailed geometric capture. Although these results do not represent a deployable system, they highlight how LiDAR and HSI could address key limitations of current practices and suggest promising directions for future research into integrated sensor-based corrosion assessment strategies. Full article

High-density polyethylene (HDPE) pipes are widely used in urban natural gas pipeline systems due to their excellent mechanical and chemical properties. However, welding joints are critical weak points in these pipelines, and defects, such as cold welding—caused by reduced temperature or/and insufficient pressure—pose significant safety risks. Traditional non-destructive testing (NDT) methods face challenges in detecting cold welding defects due to the polymer’s complex structure and characteristics. This study presents a microwave-based NDT system for detecting cold welding defects in thermal fusion welds of HDPE pipes. The system uses a focusing antenna with a resonant cavity, connected to a vector network analyzer (VNA), to measure changes in microwave parameters caused by cold welding defects in thermal fusion welds. Experiments conducted on HDPE pipes welded at different temperatures demonstrated the system’s effectiveness in identifying areas with a lack of fusion. Mechanical and microstructural analyses, including tensile tests and scanning electron microscopy (SEM), confirmed that cold welding defects lead to reduced mechanical properties and lower material density. The proposed microwave NDT method offers a sensitive, efficient approach for detecting cold welds in HDPE pipelines, enhancing pipeline integrity and safety. Full article

Post-tensioned (PT) reinforced concrete bridges are particularly vulnerable structures, as the deterioration of internal tendons is often difficult to detect using conventional inspection methods or visual assessments. This paper introduces a practical framework for ranking non-destructive testing (NDT) techniques employed to assess PT systems. The ranking is based on four performance categories: measurement accuracy, ease of use, cost, and impact of disruption to bridge operations on traffic. For each NDT technique, a score is assigned for each evaluation category, and the final ranking is determined using the weighted sum model (WSM). This approach enables the final assessment to reflect the priorities of different decision-making contexts defined by the end-user such as accuracy-oriented, cost-oriented, and impact-oriented scenarios. The proposed method is then applied to an existing bridge in order to practically demonstrate its effectiveness and the flexibility of the proposed criteria. Full article

Chemically strengthened glass is widely used for its remarkable fracture strength, mechanical performance, and scratch resistance. Assessing its hardness is crucial to evaluating improvements from chemical tempering. However, conventional methods like Vickers hardness tests are destructive, altering the sample surface. This study presents a novel, rapid, and nondestructive testing (NDT) approach by correlating the nonlinear refractive index (n₂) with surface hardness. Using ultrafast laser pulses, we measured the n₂ cross-section via the nonlinear ellipse rotation (NER) signal in Gorilla^®-type glass subjected to ion exchange (Na⁺ by K⁺). A microscope objective lens provided a penetration resolution of ≈5.5 μm, enabling a localized NER signal analysis. We demonstrate a correlation between the NER signal and hardness, offering a promising pathway for advanced, noninvasive characterization. This approach provides a reliable alternative to traditional destructive techniques, with potential applications in industrial quality control and material science research. Full article

Current non-destructive testing (NDT) methods, such as those based on wave velocity measurements, lack the sensitivity necessary to detect early-stage damage in concrete structures. Similarly, common signal processing techniques often assume linearity and stationarity among the signal data. By analyzing wave attenuation measurements using advanced signal processing techniques, mainly Hilbert–Huang transform (HHT), this work aims to enhance the early detection of damage in concrete. This study presents a novel energy-based technique that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and Hilbert spectrum analysis (HSA), to accurately capture nonlinear and nonstationary signal behaviors. Ultrasonic non-destructive testing was performed in this study on manufactured concrete specimens subjected to micro-damage characterized by internal microcracks smaller than 0.5 mm, induced through controlled freeze–thaw cycles. The recorded signals were decomposed from the time domain using CEEMDAN into frequency-ordered intrinsic mode functions (IMFs). A multi-criteria selection strategy, including damage index evaluation, was employed to identify the most effective IMFs while distinguishing true damage-induced energy loss from spurious nonlinear artifacts or noise. Localized damage was then analyzed in the frequency domain using HSA, achieving an up to 88% reduction in wave energy via Marginal Hilbert Spectrum analysis, compared to 68% using Fourier-based techniques, demonstrating a 20% improvement in sensitivity. The results indicate that the proposed technique enhances early damage detection through wave attenuation analysis and offers a superior ability to handle nonlinear, nonstationary signals. The Hilbert Spectrum provided a higher time-frequency resolution, enabling clearer identification of damage-related features. These findings highlight the potential of CEEMDAN-HSA as a practical, sensitive tool for early-stage microcrack detection in concrete. Full article

The accurate assessment of the structural condition of airfield pavements is of paramount importance to airport authorities as it determines the planning of maintenance activities. On this basis, Non-Destructive Testing (NDT) techniques provide a powerful tool to assess the mechanical properties of the individual layers of the pavement. However, information from laboratory testing of cores taken from the pavement is expected to provide a more accurate assessment of material properties. Against this background, the present research aims to investigate the accuracy of the mechanical properties of in-situ layers derived from NDT data and the associated back-calculation procedures for airfield pavements, where higher pavement thicknesses are usually required due to the high aircraft loads, while few similar studies have been conducted compared to road pavements. For this reason, the assessment of the structural condition of a flexible runway pavement is presented. The analysis shows that there is a strong correlation between the moduli estimated in the laboratory and the moduli estimated by back-calculation. Furthermore, the back-calculated moduli appear to lead to a conservative approach in assessing the structural condition of the pavement. This conservatism promotes a more proactive pavement management by airport authorities. Full article

The application of artificial intelligence within forensic image analysis marks a significant step forward for the non-destructive examination of evidence, a crucial practice for maintaining the integrity of a crime scene. While non-destructive testing (NDT) methods are established, the integration of AI, particularly for analyzing ballistic evidence, requires further exploration. This preliminary study directly addresses this gap by focusing on the use of deep learning to automate the analysis of bullet holes. This work investigated the performance of two state-of-the-art convolutional neural networks (CNNs), YOLOv8 and R-CNN, for detecting ballistic markings in digital images. The approach treats digital image analysis itself as a form of non-destructive testing, thereby preserving the original evidence. The findings demonstrate the potential of AI to augment forensic investigations by providing an objective, data-driven alternative to traditional assessments and increasing the efficiency of evidence processing. This research confirms the feasibility and relevance of leveraging advanced AI models to develop powerful new tools for Forensic Science. It is expected that this study will contribute worldwide to help (1) the police indict criminals and prove innocence; (2) the justice system judges and proves people guilty of their crimes. Full article

For non-destructive testing (NDT) of defects in aluminum alloy plates, traditional ultrasonic contact methods face challenges from high temperatures and liquid couplant contamination. Using electromagnetic acoustic transducers (EMATs), a key issue is that longitudinal waves (L-waves) excited by the butterfly-coil EMATs interfere with the desired shear waves (S-waves) reflected by internal defects. To solve this problem, a simulation–experiment approach optimized the butterfly coil parameters. An FE model visualized the electromagnetic acoustic transducer (EMAT) acoustic field and predicted signals. Orthogonal simulations tested three main parameters: excitation frequency, wire diameter, and effective coil width. Tests on aluminum specimens with artificial defects used the optimized EMAT. Simulated and measured signals showed strong correlation, validating optimal parameters. The results confirmed suppressed L-wave interference and improved defect detection sensitivity, enabling detection of a 3 mm diameter flat-bottomed hole buried 37 mm deep. Full article

Since lightweight design and construction safety is a crucial element in different sectors of industry, the use of SMA wires in composites could improve the monitoring and adjustment of mechanical properties starting from the product development process through to field use. This work shows how embedded SMA wires can lead to a better understanding of applied forces to a composite structure made of GFRP laminates. To achieve this, different methods will be addressed. Besides mechanical testing of the GFRP-samples with embedded SMA wires, NDT-methods like active thermography, high-frequency ultrasonic testing, and computer tomography are used to detect the SMA wires, whereby thermography and computer tomography are best suited. In this study, the location and the amount of the applied force on GFRP composites with embedded SMA wires could be characterized with relative resistance changes. It is shown that SMA wires with a diameter of 250 µm are preferred to wires with a diameter of 100 µm due to production process and better performance under load (4N force plateau for 100 µm in contrast to 25N force plateau for 250 µm wires). Furthermore, Young’s modulus of the GFRP composites with embedded SMA wires was measured and is similar for various samples with 30.8 GPa on average. Full article

Structural health monitoring (SHM) plays a pivotal role in ensuring the integrity and safety of critical infrastructure and mechanical components. While traditional non-destructive testing (NDT) methods offer high-resolution data, they typically require periodic access and disassembly of equipment to conduct inspections. In contrast, SHM employs permanently installed, cost-effective sensors to enable continuous monitoring, though often with reduced detail. This study presents an integrated hybrid SHM-NDT methodology enhanced by deep learning to enable the real-time monitoring and classification of mechanical stresses in structural components. As a case study, a 6-foot-long parallel flange I-beam, representing bridge truss elements, was subjected to variable bending loads to simulate operational conditions. The hybrid system utilized an ultrasonic transducer (NDT) for excitation and piezoelectric sensors (SHM) for signal acquisition. Signal data were analyzed using 1D and 2D convolutional neural networks (CNNs), long short-term memory (LSTM) models, and random forest classifiers to detect and classify load magnitudes. The AI-enhanced approach achieved 100% accuracy in 47 out of 48 tests and 94% in the remaining tests. These results demonstrate that the hybrid SHM-NDT framework, combined with machine learning, offers a powerful and adaptable solution for continuous monitoring and precise damage assessment of structural systems, significantly advancing maintenance practices and safety assurance. Full article

This study analyzed the effects of curing and maturation on the formation of shrinkage strain and destructive processes in concrete. Experimental tests were performed on commonly used concrete, class C30/37, with basalt aggregate and blast furnace cement tested: at constant temperature after water curing, at constant temperature without water curing, and under cyclically changing temperature without prior curing. Shrinkage strain was measured for 46 days with an extensometer on 150 × 150 × 600 mm specimens, and the acoustic emission (AE) method was used to monitor microcracks and processes in concrete in real time. The results were compared with the model according to EN 1992-1-1:2023. It was found that this model correctly estimates shrinkage strain for wet-curing concrete, but there are discrepancies for air-dried concrete, regardless of temperature and moisture conditions (constant/variable). Correlation coefficients between shrinkage strain increments and process increments in early-age concrete are proposed. Correlations between shrinkage strain and destructive processes occurring in concrete were confirmed. It was found that by using correlation coefficients, it is possible to estimate internal damage in relation to shrinkage strain. The results indicate the need to develop guidelines for estimating shrinkage strain in non-model environmental conditions and demonstrate the usefulness of the nondestructive AE method in diagnosing early damage, especially in concrete structures exposed to adverse service conditions. Full article