Search Results (319)

The internal quality of watermelons has garnered extensive attention. Conventional destructive quality detection for watermelons causes fruit loss, while existing acoustic techniques often rely on a single evaluation index. To address these issues, this study proposes a non-destructive method for comprehensive watermelon quality detection using acoustic and vibration signals. Signals from two watermelon varieties were collected under impact excitation to extract six time-domain and frequency-domain features. Factor Analysis of Mixed Data (FAMD) was employed to integrate ripeness, Soluble Solids Content (SSC), firmness, and sensory scores into a Comprehensive Quality Index (CQI), categorizing samples into High-Quality, Medium-Quality, and Low-Quality groups. Following physically constrained data augmentation to mitigate small sample size and class imbalance, an Extremely Randomized Trees (Extra-Trees) model was constructed. Results demonstrate that the Extra-Trees model achieved an overall testing accuracy of 0.92, with recall rates of 0.93 and 1.00 for Low-Quality and High-Quality watermelons, respectively. Recognition for Medium-Quality samples was lower due to overlapping physical and acoustic characteristics. Ultimately, this system aligns with actual consumer demands, providing technical support for low-cost, portable, and non-destructive watermelon grading. Full article

The mechanical properties and real-time damage evolution of sustainable concrete (SC) containing 100% recycled concrete aggregate (RCA) under the combined action of hybrid steel and polyolefin fibers were studied. Inspired by solving the massive effects on the environment from construction waste, as well as to improve the lower mechanical performance of lower-grade RCA, the effect of combining high-stiffness hooked-end steel fibers and flexible macro-polyolefin fibers within RCA was investigated. Six different mix designs were considered: plain, single-fiber (100% steel and 100% polyolefin) and three hybrid composites with varying fractions of the steel/polyolefin fibers (25/75, 50/50, and 75/25). Compressive, tensile and flexural strengths were determined by mechanical testing. During compressive testing, the damage evolution was monitored using low-cost acoustic emission (AE) as a non-destructive technique. Cumulative hits analysis, amplitude distributions, and the statistical b-value parameter were used for damage characterization. The results show that steel fiber significantly increased compressive strength (an increase of up to 13.8%), and the 50/50 hybrid mix showed a high synergistic effect, yielding the highest tensile (4.86 MPa) and flexural (25.54 MPa) strengths. AE analysis identified different damage fingerprints: Based on amplitude analysis, steel-fiber composites exhibited high-amplitude events (which may be attributable to fiber pull-out); polyolefin-fiber composites generated medium-amplitude events (may have resulted from distributed microcracking); and hybrid mixes displayed a mixed amplitude distribution. The b-value analysis provided insight into progressive damage and revealed that the hybrid fibers induce stable, diffuse damage that prevents the brittle failure of plain recycled aggregate concrete (RAC). The results show that hybrid fiber reinforcement can be a reliable approach to enhance the mechanical performance and crack resistance of RAC. Furthermore, low-cost acoustic emission (AE) serves as an effective non-destructive method for monitoring damage progression within the material. Full article

The detection and degradation analysis of subsurface microcracks in mural paintings remain challenging due to their inhomogeneous multilayered structure and complex deterioration mechanisms. In this study, we propose a multimodal stepwise method for three-dimensional characterization and cross-scale degradation analysis by integrating digital holography (DH), infrared thermography (IRT), acoustic excitation (AE), and molecular dynamics (MD) simulations. In the first step, an adjustable field-of-view (FOV) digital holographic system is developed to capture subsurface deformation under acoustic excitation, enabling high-resolution planar characterization of subsurface microcracks. Infrared thermography is then employed to estimate crack depth through an inverse thermal model, achieving full three-dimensional reconstruction of crack geometry. Based on the reconstructed structures, MD simulations are conducted to investigate the evolution of stress, bond breaking, and crack propagation under varying temperature and humidity conditions, with particular emphasis on water molecule migration and chemically induced degradation. The results demonstrate that environmental factors promote stress concentration and material embrittlement at crack tips, leading to secondary microcrack formation and progressive deterioration. Experimental aging tests show strong agreement with simulation results, validating the proposed methodology. This work establishes a unified “characterization–simulation–validation” paradigm, providing new insights into the mechanisms of mural degradation and offering a robust framework for non-destructive evaluation and preventive conservation of multilayer cultural heritage materials. Full article

Chloride and sulfate ion attacks are among the leading causes of deterioration in reinforced concrete structures, leading to the corrosion of steel reinforcement, expansion, cracking, and premature structural failure. Early detection of these ion-induced deteriorations is essential not only for maintaining safety but also for supporting sustainability objectives by extending service life, reducing material consumption, and minimizing carbon-intensive repairs. This review synthesizes current advances in non-destructive testing (NDT) techniques used to identify and quantify the impacts of chloride and sulfate ions in reinforced concrete. The mechanisms of ion ingress and their associated degradation processes are examined together with the operating principles, strengths, and limitations of key NDT methods, including electrical resistivity, acoustic emission, infrared thermography, ground penetrating radar, and ultrasonic pulse velocity. By enabling timely maintenance decisions and reducing unnecessary demolition or intrusive testing, these NDT methods contribute directly to sustainable infrastructure management. Through comparative analysis and real-world case studies, the paper highlights the most effective NDT applications for deterioration scenarios and outlines emerging innovations that enhance accuracy, data interpretation, and long-term monitoring capabilities. The findings demonstrate how advancements in NDT support the development and preservation of durable and sustainable concrete structures. Full article

The demand for efficient combustion in boilers drives the development of ultra-supercritical power plants. P92 steel pressure, and pipelines operate in high-temperature and high-pressure environments and are prone to high-temperature creep damage. Non-destructive testing is a key method to ensure the safety of the pipe. However, existing non-destructive testing methods are difficult to achieve non-destructive detection of creep damage. Creep damage affects magnetic Barkhausen noise (MBN) and magneto-acoustic emission (MAE) signals; therefore, it is possible to evaluate creep damage using these signals. This article first establishes a theoretical model for MBN and MAE. Afterward, the influence of magnetizing waveform, amplitude, and frequency on MBN and MAE signals was studied through experiments. Finally, by analyzing the characteristics of MBN and MAE signals, the optimal magnetization conditions and signal characteristic parameters for detecting creep damage using MBN and MAE signals were determined. The experimental results also confirmed the correctness of the theoretical model. Full article

This study explores non-destructive techniques for characterizing the mechanical properties of materials pertinent to cultural heritage, emphasizing the preservation of sample integrity. A modified acoustic resonance method (MARM), utilizing a two-microphone configuration, is introduced for the simultaneous, fully non-contact determination of dynamic elastic modulus and damping (loss factor). The method is validated through comparison with the impulse excitation technique (IET) and ultrasonic pulse velocity testing (UT). The approach is applied to two material categories exhibiting contrasting porosities: dense natural stone and highly porous unfired clay. Results demonstrate strong concordance among all methods for stone, confirming the reliability of non-destructive techniques for homogeneous materials. Conversely, unfired clay displays greater variability attributable to its heterogeneous and porous nature, alongside increased damping. This investigation reveals that conventional modulus–strength correlations are not directly applicable to unfired clay. To address this, a simplified strength estimation model incorporating the estimated elastic modulus and porosity is proposed. The model achieves improved alignment with experimental data and delineates applicability boundaries for porous materials. The presented framework facilitates consistent, non-destructive evaluation of mechanical properties, with notable implications for the assessment and preservation of cultural heritage materials. Full article

The nondestructive characterization of coating thickness, acoustic velocity, and density is essential for industrial quality control. However, conventional ultrasonic reflection coefficient amplitude spectrum (URCAS)-based inversion methods typically require prior knowledge of acoustic attenuation, which is often unavailable for thin coatings and limits their practical applicability. To address this issue, a nonlinearity-guided dual-spectrum inversion framework is proposed by combining the URCAS with a layer-phase spectrum. It is found that the layer-phase spectrum exhibits strong nonlinear sensitivity to variations in acoustic velocity and density, which helps improve parameter identifiability. Based on this property, an improved particle swarm optimization algorithm is developed to enable simultaneous inversion of thickness, velocity, and density without explicit prior attenuation information. Finite-element simulations show that the conventional URCAS method yields mean relative errors exceeding 5%, whereas the proposed method reduces these errors to below 3% under the tested conditions. Experimental validation on eight industrial polytetrafluoroethylene (PTFE) coatings with thicknesses ranging from 20.89 μm to 120.11 μm (down to approximately 0.11 λ at 10 MHz) demonstrates that the proposed method achieves average relative errors within 10% and improves inversion accuracy by about 6% compared with amplitude-only approaches. The results indicate that the proposed attenuation-independent and nonlinearity-guided strategy provides an effective solution for the quantitative nondestructive evaluation of subwavelength coatings. The method is particularly suitable for thin coatings with unknown attenuation. Full article

Concrete wall cavities are common hidden defects in construction engineering that seriously reduce structural safety, durability, and construction quality, especially in old buildings and projects without complete design documents. Traditional detection methods have obvious limitations: the manual tapping method relies heavily on subjective experience and lacks quantitative standards, while advanced non-destructive testing methods such as ultrasonic testing and infrared thermography are expensive, complex to operate, and difficult to apply on a large scale. At present, the quantitative correlation between acoustic signal characteristics and cavity defects has not been fully studied. To address these problems, this study combines literature analysis, controlled experiments, and acoustic signal processing to propose a quantitative identification method for concrete wall cavities based on autocorrelation analysis of sound signals. Tapping signals from normal and cavity walls are collected and processed using band-pass filtering and amplitude normalization. The autocorrelation function (ACF) is then used to extract characteristic parameters. The results show that the proposed method exhibits significantly improved accuracy and efficiency compared with traditional manual detection. Obvious differences in autocorrelation characteristics can be observed between normal and cavity walls. The method realizes the transformation from subjective auditory judgment to objective quantitative identification, with low cost, strong anti-interference ability, and high sensitivity to small defects. It provides a reliable technical tool for the rapid and quantitative non-destructive testing of concrete wall cavities in engineering practice. Full article

A substantial share of reinforced-concrete infrastructure assets has reached an age where deterioration mechanisms such as cracking, delamination, and voiding may develop, potentially increasing safety risks and maintenance demands. Conventional condition assessment commonly relies on localized intrusive testing (e.g., coring) and manual sounding, which can be disruptive, labor-intensive, and partly subjective. Vibration-based Non-Destructive Testing (NDT) provides an alternative by exciting the structure and evaluating changes in its dynamic response. In contrast to previous studies, which typically assess a single excitation method in isolation, this study provides a systematic side-by-side comparison of three vibration-based NDT excitation approaches: mechanical impact using a custom compressed-air impact device, acoustic excitation, and shaker excitation. All three methods were evaluated under identical measurement conditions. The vibration response is measured using Laser Doppler Vibrometry (LDV), enabling non-contact acquisition of frequency-response signatures. A custom mechanical excitation device was developed and evaluated, and the results indicate that it provides stable and repeatable excitation with good defect discrimination. Experiments on specimens with representative defect types show that mechanical impact and shaker excitation yield the most repeatable and discriminative response features, whereas acoustic excitation provides insufficient signal-to-noise ratios (SNRs) for the smallest tested specimens. Among the evaluated setups, the Qsources surface-mounted shaker and the compressed-air impact device provided the most promising laboratory results. However, the large electrodynamic shaker was used mainly as a controlled reference excitation method, and scalable field inspection would require more compact and automated excitation solutions. The goal of this work is therefore to support the development of efficient LDV-based non-contact inspection methods for safer and more reliable monitoring of reinforced-concrete infrastructure. Full article

Seagrasses are vital ecosystem engineers and habitat architects in coastal environments, with Posidonia oceanica in the Mediterranean playing a crucial role as an indicator of ecological health. As an endemic and vulnerable species, P. oceanica meadows are highly susceptible to environmental degradation, underscoring the importance of non-destructive monitoring techniques. Traditional SCUBA-based surveys are accurate but resource-intensive and difficult to scale, especially for estimating shoot density and leaf length. This study applies a conservative acoustic-based approach to assess Posidonia oceanica biometrics, habitat characteristics, and ecological status along the Turkish Levant coast. The method offers a non-destructive alternative to SCUBA surveys and addresses a regional knowledge gap in Mediterranean seagrass monitoring. Acoustic data collected during winter and summer 2019 along the Turkish Levant coast were analyzed to estimate seagrass biometrics and derive ecological indicators, with validation via SCUBA observations. Results show that acoustic methods can reliably estimate shoot density, leaf area index, and canopy height. They provide broad-scale coverage and efficiency, though further refinement is required to improve calibration across depths and substrates. While acoustic methods provide broad, non-invasive coverage, they are affected by spatial and temporal variability that SCUBA surveys capture more reliably. Calibration of the POSIBIOM (vers 1.1) algorithm was based on specimens collected at 15 m depth on rocky substrates. While this provided consistent regression relationships, it may limit accuracy when extrapolated to habitats such as sand, mud, or matte. This study represents the first high-resolution, spatiotemporal mapping of P. oceanica meadows and benthic habitats along a significant portion of the Turkish Levant coast using acoustics alone. Overall, the study highlights the potential of acoustics as a scalable, non-invasive tool for seagrass monitoring. This approach contributes to ecosystem-based management and conservation strategies in the Mediterranean. Future work will focus on refining models to address bottom type- and depth-dependent acoustic responses and improve biometric accuracy. Full article

Acoustic emission (AE) is a vital non-destructive technique for monitoring damage in materials, yet its simulation via the Discrete Element Method (DEM) has historically been limited to material-scale analysis. This research presents a novel application of block-based DEM to simulate AE signals in masonry structures at the structural scale under quasi-static in-plane loading. Using a simplified micro-modeling approach, the study first validates the method by monitoring crack initiation and AE energy in single mortar bed joints under tensile and shear conditions. The methodology is then scaled to a large-scale masonry wall panel (1.835 × 1.170 × 0.15 m³) subjected to monotonic shear loading. A critical finding is the influence of local damping; a reduced damping ratio of 0.3 is recommended to preserve the kinetic energy necessary for capturing clear velocity signals. Numerical results show strong agreement with experimental force-displacement and cumulative AE energy curves, confirming the model’s robustness. Furthermore, frequency analysis of the simulated signals successfully distinguishes between tensile and shear failure modes. This study fills a significant gap in the literature by demonstrating that DEM is an effective predictive tool for structural-scale failure analysis and AE monitoring in heterogeneous masonry. Full article

This review paper presents a comprehensive and system-oriented analysis of advanced non-destructive testing (NDT) technologies for metal additive manufacturing (AM), including X-ray computed tomography (XCT), ultrasonic testing (UT), infrared thermography, acoustic emission (AE), and electromagnetic techniques. While the existing literature often focuses on the physical principles of individual NDT methods, this work addresses a critical knowledge gap by analyzing NDT as a digitally integrated “quality intelligence layer” rather than a standalone post-process inspection tool. The primary motivation is to bridge the disconnect between raw inspection data and cyber–physical production systems. Particular focus is given to NDT data analytics and digitalization, where machine learning (ML) and digital twin (DT) integration are discussed as fundamental enablers of intelligent manufacturing. The review systematically examines image and signal processing pipelines required for quantitative defect characterization, highlighting challenges related to voxel resolution, signal-to-noise ratio, anisotropic microstructures, and operator dependency. It further analyzes supervised learning, deep learning, and multi-sensor data fusion approaches for automated defect classification and predictive quality assessment. Furthermore, the role of digital twins in coupling in situ monitoring data, ex situ NDT results, and physics-based models is discussed as a transformative pathway toward closed-loop process control and evidence-based certification. By synthesizing NDT science with digital manufacturing architectures, this review contributes a unique framework for transitioning from traditional inspection-centric quality control to a predictive, adaptive, and digital twin-enabled quality assurance paradigm. The work concludes by identifying key research gaps in data standardization and computational scalability, providing a strategic roadmap for the future of smart AM production. Full article

Bolt loosening is a critical precursor to structural failure in major industrial and transportation equipment. Although acoustic non-destructive testing (NDT) offers a cost-effective diagnostic solution, its practical deployment is often hindered by low signal-to-noise ratios (SNRs) and the limited ability of conventional models to isolate fine-grained transient acoustic signatures from complex background interference. To address these challenges, this paper proposes PCT-Net, a multi-scenario noise-adaptive fusion network for bolt-state recognition. First, an Adaptive Spectral Masking mechanism is introduced as a data augmentation strategy. Instead of rigid zero-padding, it dynamically blends local spectral energies to encourage the learning of more robust and noise-invariant representations. Furthermore, rather than simply concatenating multiple modules, PCT-Net adopts a synergistic feature extraction framework to decouple complex acoustic signatures. A perceptual frontend is used to establish acoustically meaningful representation priors. To handle the highly dispersed characteristics of loosening signals, cascaded convolutional modules progressively suppress redundant environmental interference while capturing high-frequency local transient impacts. Meanwhile, to overcome the limited receptive field of convolutional operations, an embedded Transformer mechanism is introduced to model long-range temporal dependencies and low-frequency structural variations throughout the tapping cycle. By integrating local fine-grained transient modeling with global structural dependency modeling, the proposed network can better distinguish subtle decision boundaries among different loosening states. Extensive experiments show that PCT-Net achieves a classification accuracy of 97.12% under standard conditions and maintains stable performance under severe noise scenarios. These results demonstrate the effectiveness of the proposed method and highlight its potential for intelligent industrial safety monitoring. Full article

Acoustic emission (AE) and digital image correlation (DIC) techniques enable real-time capture of damage signals and full-field deformation at anchored rock–concrete interfaces under shear loading, which is critical for quantitatively characterizing freeze–thaw (F-T) degradation and preventing geological disasters in cold regions. This study synchronously monitored full-shear-process AE signals using a broadband AE system (150 kHz resonant frequency, 5 MS/s sampling) and captured high-precision full-field deformation via a 5-megapixel monocular DIC system (25 fps). F-T cycle and direct shear tests were conducted on sandstone–concrete anchored specimens with varying F-T cycles and anchor depths to investigate their effects on shear mechanical properties, AE characteristics and failure modes. Results show that AE peak ring count first decreases by 44.9% then increases by 56.5%, while cumulative ring count exhibits a three-stage evolution. Shear crack proportion first decreases then increases, with tensile failure remaining dominant throughout. DIC reveals that F-T cycles shift failure from crack propagation to surface delamination and interface slip, while different anchor depths induce distinct failure patterns. This study confirms that AE and DIC can accurately characterize F-T degradation, providing a reliable non-destructive monitoring method for cold-region anchorage engineering. Full article

Acoustic imaging, especially ultrasound, underpins a wide range of applications from non-destructive evaluation to medical and materials analysis, yet its performance is ultimately constrained by lateral resolution. This review systematically summarizes recent advances in overcoming diffraction-limited resolution, encompassing traditional focusing techniques, transducer optimization, physical metamaterial lenses, and methods based on algorithmic optimization and deep learning technologies. It comprehensively covers approaches for enhancing acoustic lateral resolution, compares the differences and respective advantages and disadvantages of various methods, and proposes clear directions and recommendations for future research. This work provides robust guidance for subsequent research trends and development opportunities in higher-resolution acoustic imaging. Full article