Search Results (153)

The magnetic flux leakage (MFL) method is widely acknowledged as a highly effective non-destructive evaluation (NDE) technique for detecting local damage in ferromagnetic structures such as steel wire ropes. In this study, a multi-channel MFL sensor module was developed, incorporating a purpose-designed Hall sensor array and magnetic yokes specifically shaped for steel cables. To validate the proposed damage detection method, artificial damages of varying degrees were inflicted on wire rope specimens through experimental testing. The MFL sensor module facilitated the scanning of the damaged specimens and measurement of the corresponding MFL signals. In order to improve the signal-to-noise ratio, a comprehensive set of signal processing steps, including channel equalization and normalization, was implemented. Subsequently, the detected MFL distribution surrounding wire rope defects was transformed into MFL images. These images were then analyzed and processed utilizing an object detection method, specifically employing the YOLOv9 network, which enables accurate identification and localization of defects. Furthermore, a quantitative defect detection method based on image size was introduced, which is effective for quantifying defects using the dimensions of the anchor frame. The experimental results demonstrated the effectiveness of the proposed approach in detecting and quantifying defects in steel cables, which combines deep learning-based analysis of MFL images with the non-destructive inspection of steel cables. Full article

This study intends to understand the effect of annealing behavior on the microstructure and mechanical and magnetic properties of cold-rolled extra-low-carbon steel. Deformed steel samples are annealed at temperature ranges of 200–690 °C followed by air-cooling. As part of this study, Magnetic Hysteresis loop (MHL) and Barkhausen emission (MBE) measurements are carried out for non-destructive evaluation (NDE) of the mechanical properties that are altered during annealing, viz. recovery and recrystallization. At low annealing temperature ranges 200 < T < 550 °C, the recovery causes no substantial variations in microstructure, hardness value from 191–185 HV, and tensile strength 456–452 MPa, while magnetic coercivity decreases from 293–275 A/m for cold-rolled annealed steels. The microstructural changes due to recovery and recrystallization are examined using transmission electron microscopy and orientation imaging microscopy (OIM) through electron backscattered diffraction (EBSD). Recrystallization is found after annealing at T > 550 °C, confirmed by the lowering of the microstructural KAM value from 0.81° to 0.65° and a hardness drop from 190.02 to 98 HV for cold-rolled extra-low-carbon steel. Full article

Non-destructive testing (NDT) and non-destructive evaluation (NDE) are essential tools for ensuring the structural integrity, safety, and reliability of critical systems across the aerospace, civil infrastructure, energy, and advanced manufacturing sectors. As engineered materials evolve into increasingly complex architectures such as fiber-reinforced polymers, fiber–metal laminates, sandwich composites, and functionally graded materials, traditional NDT techniques face growing limitations in sensitivity, adaptability, and diagnostic reliability. This comprehensive review presents a multi-dimensional classification of NDT/NDE methods, structured by physical principles, functional objectives, and application domains. Special attention is given to hybrid and multi-material systems, which exhibit anisotropic behavior, interfacial complexity, and heterogeneous defect mechanisms that challenge conventional inspection. Alongside established techniques like ultrasonic testing, radiography, infrared thermography, and acoustic emission, the review explores emerging modalities such as capacitive sensing, electromechanical impedance, and AI-enhanced platforms that are driving the future of intelligent diagnostics. By synthesizing insights from the recent literature, the paper evaluates comparative performance metrics (e.g., sensitivity, resolution, adaptability); highlights integration strategies for embedded monitoring and multimodal sensing systems; and addresses challenges related to environmental sensitivity, data interpretation, and standardization. The transformative role of NDE 4.0 in enabling automated, real-time, and predictive structural assessment is also discussed. This review serves as a valuable reference for researchers and practitioners developing next-generation NDT/NDE solutions for hybrid and high-performance structures. Full article

Precast concrete structures have become increasingly popular in the construction industry due to their ability to enhance efficiency, structural soundness, quality, and sustainability. Among these, modular construction has emerged as a transformative approach that fully leverages precast technology by manufacturing 3D modules off-site and assembling them on-site using inter-module connections. This study reviewed current literature trends on precast concrete structures and modular construction, analysing how modular construction distinguishes itself from other precast systems. This review further emphasises the role of composite connections—grouted, bolted, and hybrid systems—critical in ensuring structural integrity, efficiency in load transfer, and seismic resilience in modular construction. Advancements in composite connections have demonstrated significant promise, particularly in seismic performance, with reported energy dissipation improvements of up to 30% in hybrid connection systems. Yet limitations still exist, necessitating improvements in load transfer efficiency, ductility, and reliability under dynamic loads. Additionally, design considerations for modular construction, such as modular configurations, handling stresses, and transportation challenges, are explored to highlight their influence on system performance. This review underscores the feasibility and potential of modular construction in fostering sustainable and resilient infrastructure, as studies indicate that modular construction can reduce project timelines by up to 50% while minimising material waste by approximately 30%. The role of Non-Destructive Evaluation (NDE) techniques and intelligent monitoring systems in assessing and enhancing the lifecycle performance of composite connections is also emphasised. This review further advocates for continued research to refine composite connections and support the broader adoption of modular construction in modern building practices. Full article

Non-destructive evaluation (NDE) is highly relevant to assessing micro- and macrostructural changes in ferritic and ferritic/martensitic steels subjected to high temperature loading. These materials are widely used in energy generation, where they undergo extreme thermal and mechanical loads. This study examines the feasibility of micromagnetic NDE techniques, i.e., micromagnetic measurements, supported by machine learning methods, to identify and characterize the micro- and macrostructural changes caused by the mechanical loading at high temperatures of power plant steels, i.e., ferritic/martensitic P91 and the high chromium ferritic steel HiperFer-17Cr2. While the P91 did not show any systematic changes in micromagnetic measurements, which generally correlate with the evolution of the microstructure and the mechanical properties, for the HiperFer-17Cr2, pronounced changes in the micromagnetic properties were observed. In correlation with the evolution of the hardness and cyclic deformation behavior, which are both mainly attributed to Laves phase precipitation, the micromagnetic measurements significantly changed depending on the temperature, number of load cycles and load amplitude applied. Thus, these NDE methods can be used for early diagnosis and preventive maintenance strategies for HiperFer-17Cr2, potentially extending the lifespan of the components and mitigating safety risks. Full article

This paper focuses on a midstream pipeline to help us develop a better understanding of Pipeline Inspection Gauge (PIG) operation. A methodological combination of non-destructive testing (NDT), non-destructive evaluation (NDE), and risk-based inspection (RBI) was applied within an engineering system compatible with industry standards. In this sense, the implementation of the protocol and an assessment of the effectiveness of the proposed research model for solving problems that occur during a PIG’s working life, such as damage mechanisms and methods for its repair, are presented. The RBI methodology is derived using two mutually validating approaches to provide a result with low uncertainty. The result of this research confirms the expediency of the multi-perspective research approach and demonstrates the applicability of this methodology through a model study in the area of protocol creation—an essential aspect of ensuring the safety of pipeline inspections. Full article

Additive manufacturing (AM) has revolutionized production across industries, yet persistent challenges in defect detection and process reliability necessitate advanced in situ monitoring solutions. While non-destructive evaluation (NDE) techniques such as X-ray computed tomography, thermography, and ultrasonic testing have been widely adopted, the critical role of 3D surface topographic monitoring remains underutilized for real-time anomaly detection. This work comprehensively reviews the 3D surface monitoring of AM processes, such as Laser powder bed fusion, directed energy deposition, material extrusion, and material jetting, highlighting the current state and challenges. Furthermore, the article discusses the state-of-the-art advancements in closed-loop feedback control systems, sensor fusion, and machine learning algorithms to integrate 3D surface data with various process signatures to dynamically adjust laser parameters and scan strategies. Guidance has been provided on the best 3D monitoring technique for each of the AM processes. Motivated by manufacturing labor shortages, the high skill required to operate and troubleshoot some of these additive manufacturing techniques, and zero-defect manufacturing goals, this paper also explores the metamorphosis towards autonomous AM systems and adaptive process optimization and explores the role and importance of real-time 3D monitoring in that transition. Full article

In the field of nondestructive evaluation (NDE) using ultrasonic guided waves, accurately assessing the aging failure of insulation coatings remains a challenging and prominent research topic. While the application of ultrasonic guided waves in material testing has been extensively explored in the existing literature, there is still a significant gap in quantitatively evaluating the aging failure of insulation coatings. This study innovatively proposes an NDE method for assessing insulation coating aging failure by integrating signal processing and machine learning technologies, thereby effectively addressing both theoretical and practical gaps in this domain. The proposed method not only enhances the accuracy of detecting insulation coating aging failure but also introduces new approaches to non-destructive testing technology in related fields. To achieve this, an accelerated aging experiment was conducted to construct a cable database encompassing various degrees of damage. The effects of aging time, temperature, mechanical stress, and preset defects on coating degradation were systematically investigated. Experimental results indicate that aging time exhibits a three-stage nonlinear evolution pattern, with 50 days marking the critical inflection point for damage accumulation. Temperature significantly influences coating damage, with 130 °C identified as the critical threshold for performance mutation. Aging at 160 °C for 100 days conforms to the time-temperature superposition principle. Additionally, mechanical stress concentration accelerates coating failure when the bending angle is ≥90°. Among preset defects, cut defects were most destructive, increasing crack density by 5.8 times compared to defect-free samples and reducing cable life to 40% of its original value. This study employs Hilbert–Huang Transform (HHT) for noise reduction in ultrasonic guided wave signals. Compared to Fast Fourier Transform (FFT), HHT demonstrates superior performance in feature extraction from ultrasonic guided wave signals. By combining HHT with machine learning techniques, we developed a hybrid prediction model—HHT-LightGBM-PSO-SVM. The model achieved prediction accuracies of 94.05% on the training set and 88.36% on the test set, significantly outperforming models constructed with unclassified data. The LightGBM classification model exhibited the highest classification accuracy and AUC value (0.94), highlighting its effectiveness in predicting coating aging damage. This research not only improves the accuracy of detecting insulation coating aging failure but also provides a novel technical means for aviation cable health monitoring. Furthermore, it offers theoretical support and practical references for nondestructive testing and life prediction of complex systems. Future studies will focus on optimizing model parameters, incorporating additional environmental factors such as humidity and vibration to enhance prediction accuracy, and exploring lightweight algorithms for real-time monitoring. Full article

The governing Partial Differential Equation (PDE) for wave propagation or the wave equation involves multi-scale and multi-dimensional oscillatory phenomena. Wave PDE challenges traditional computational methods due to high computational costs with rigid assumptions. The advent of scientific machine learning (SciML) presents a novel paradigm by embedding physical laws within neural network architectures, enabling efficient and accurate solutions. This study explores the evolution of SciML approaches, focusing on PINNs, and evaluates their application in modeling acoustic, elastic, and guided wave propagation. PINN is a gray-box predictive model that offers the strong predictive capabilities of data-driven models but also adheres to the physical laws. Through theoretical analysis and problem-driven examples, the findings demonstrate that PINNs address key limitations of traditional methods, including discretization errors and computational inefficiencies, while offering robust predictive capabilities. Despite current challenges, such as optimization difficulties and scalability constraints, PINNs hold transformative potential for advancing wave propagation modeling. This comprehensive study underscores the transformative potential of PINN, followed by recommendations on why and how it could advance elastic, acoustic, and guided wave propagation modeling and sets the stage for future research in the field of Structural Health Monitoring (SHM)/Nondestructive Evaluation (NDE). Full article

Non-destructive examination (NDE) in the field of materials engineering is a technique based on acoustics and optical principles used for detecting and evaluating internal defects in materials without causing any damage. The majority of current research on material damage focuses on the analysis of a single NDE method, resulting in low correlation between different NDE methods, and their results are frequently presented as complex data and images, making it difficult for professionals to obtain intuitive inspection results. Therefore, we propose a visual analytics system, NDEExplorer, aimed at solving these problems through visual analytics techniques. The system supports the use of two NDE methods, Acoustic Emission (AE) and Digital Image Correlation (DIC), providing interactive and intuitive views for observing composite material damage features. In addition, the system features a fusion analysis approach and a view that combines AE and DIC methods, enabling users to explore the correlations and trends in multimodal data generated during the material damage process. For users, the application of this system can help accurately identify the various material damage stages and their accompanying damage modes. To evaluate the effectiveness of the proposed method, we conduct a case study using two modal datasets from the same composite material damage scenario and carry out qualitative interviews with professionals and graduate students in the field. Finally, the quantitative feedback from a user study confirms the usefulness of our visual system for the multimodal analysis of material damage datasets. Full article

Concrete bridges are the most prevalent bridge type worldwide, forming critical components of transportation infrastructure. These bridges are subjected to continuous deterioration due to environmental exposure and operational stresses, necessitating ongoing condition monitoring. Despite extensive research on condition rating and deterioration modeling of concrete bridges, a comprehensive and comparative understanding of these processes remains underexplored. This paper addresses this gap by conducting a critical scientometric and systematic review of condition rating and deterioration modeling approaches for concrete bridges to highlight their strengths and limitations. Accordingly, most of the condition rating methods were found to have a heavy reliance on qualitative visual inspections (VI) and inherent subjective assumptions. Techniques such as fuzzy logic and non-destructive evaluation (NDE) methods were identified as promising tools to mitigate uncertainties and enhance accuracy. Moreover, the performance of most deterioration models was dependent on the quality of the historical condition data. The advancement of hybrid deterioration models, such as integrating artificial intelligence (AI) with stochastic and physics-based approaches, has proven to be a powerful strategy, combining the strengths of each method to deliver enhanced condition predictions. Finally, this study offers key insights and future research directions to assist researchers and policymakers in advancing sustainable concrete bridge management practices. Full article

Infrastructure maintenance is critical to ensuring public safety and the longevity of essential structures. Nondestructive Evaluation (NDE) techniques allow for infrastructure inspection without causing damage. Computer vision has emerged as a powerful tool in this domain, providing automated, efficient, and accurate solutions for defect detection, structural monitoring, and real-time analysis. This review explores the current state of computer vision in NDE, discussing key techniques, applications across various infrastructure types, and the integration of deep learning models such as convolutional neural networks (CNNs), vision transformers (ViTs), and hybrid models. The review also highlights challenges, including data availability and scalability. It proposes future research directions, including real-time monitoring and the integration of Artificial Intelligence (AI) with Internet of Things (IoT) devices for comprehensive inspections. Full article

Fire incidents pose significant threats to the structural integrity of reinforced concrete buildings, often necessitating comprehensive rehabilitation to restore safety and functionality. Effective rehabilitation of fire-damaged structures relies heavily on accurate damage assessment, which can be challenging with traditional invasive methods. This paper explores the impact of severe damage due to fire exposure on the mechanical behavior of steel-fiber-reinforced concrete (SFRC) using nondestructive evaluation (NDE) techniques. After being exposed to direct fire, the SFRC specimens are subjected to fracture testing to assess their mechanical properties. NDE techniques, specifically acoustic emission (AE) and ultrasonic pulse velocity (UPV), are employed to assess fire-induced damage. The primary aim of this study is to reveal that AE parameters—such as amplitude, cumulative hits, and energy—are strongly correlated with mechanical properties and damage of SFRC due to fire. Additionally, AE monitoring is employed to assess structural integrity throughout the loading application. The distribution of AE hits and the changes in specific AE parameters throughout the loading can serve as valuable indicators for differentiating between healthy and thermally damaged concrete. Compared to the well-established relationship between UPV and strength in bending and compression, the sensitivity of AE to fracture events shows its potential for in situ application, providing new characterization capabilities for evaluating the post-fire mechanical performance of SFRC. The test results of this study reveal the ability of the examined NDE methods to establish the optimum rehabilitation procedure to restore the capacity of the fire-damaged SFRC structural members. Full article

This study demonstrated the potential of a non-destructive evaluation (NDE) method to assess the elastic properties of materials printed under various parameters. A database was created documenting the relationship between the elastic properties (Young’s modulus, shear modulus, and Poisson’s ratio) of PLA (polylactic acid) materials and selected printing parameters such as temperature, speed, and layer height. PLA, which is widely used in additive manufacturing, offers convenient testing conditions due to its less demanding control compared to materials like metals. Ultrasonic testing was conducted on specimens printed under different nozzle temperatures, speeds, and layer heights. The results indicated that an increase in the printing temperature corresponded to an increase in material density and elastic properties of the material. In contrast, an increase in layer height led to a decrease in both density and the elastic properties of the material. Variations in the nozzle speed had a negligible effect on density and did not show a notable effect on the elastic moduli. This study demonstrated that ultrasonic testing is effective in measuring the elastic properties of PLA materials and shows the potential of real-time ultrasonic NDE. Full article