Authors: Enzo Rizzo Federica Zanotto Giacomo Fornasari Sofia Rando Francesca Gallo Andrea Balbo Vincenzo Grassi
Reinforced concrete structures, once considered very durable and capable of withstanding a variety of adverse environmental conditions, often suffer from premature reinforcement corrosion, compromising their safety and serviceability. Ensuring the safety of bridges and buildings requires effective, non-destructive inspection and monitoring techniques to assess the state of degradation without damaging the integrity of the asset. Although a wide range of non-destructive testing (NDT) methods is currently available, few are capable of identifying durability issues during the initial stages before the damage becomes critical. To address this gap, this paper describes an innovative laboratory experiment based on an integrated approach that combines Ground-Penetrating Radar (GPR) and electrochemical methods. This research represents an advanced step in our ongoing projects, merging geophysical and electrochemical expertise to enhance diagnostic precision. A reinforced cement mortar specimen was subjected to free corrosion via partial immersion in sodium chloride solutions of varying concentrations (1, 10, and 35 g/L), followed by an accelerated corrosion phase. The phenomenon was monitored simultaneously using GPR and electrochemical tests. Each technique provided specific information, but a data integration method used in the operating system will further improve the overall quality of diagnosis. Specifically, the application of the Hilbert Transform to GPR signals allowed for a correlation between envelope amplitude variations and the electrochemical behavior of the rebars. These laboratory results highlighted that an integrated observation was useful to indirectly observe the evolution of the phenomenon of corrosion in the steel reinforcement embedded in the mortar specimens.
]]>Authors: Yexin Zhou Yoichi Ohyanagi Akiko Iwata Koji Shibazaki Kazuhito Murakami
Accurate paper fiber identification is essential for cultural heritage conservation. Traditional staining methods are destructive, while macroscopic AI models often lack physicochemical interpretability. This study explores the feasibility of a non-destructive analytical approach using micro-hyperspectral imaging (Micro-HSI) to overcome both limitations. Three traditional Japanese bast fibers, Kozo, Mitsumata, and Gampi, were analyzed as standard reference samples. Relative reflectance spectra were extracted from microscopic fiber regions using Micro-HSI. Dynamic normalization and Savitzky–Golay first-derivative filtering were applied to suppress scattering effects and baseline drift. Principal component analysis (PCA) and linear discriminant analysis (LDA) were applied in parallel for dimensionality reduction and supervised classification, respectively. The results indicated that unsupervised PCA exhibited substantial inter-class overlap because of the shared cellulose matrix among the fiber types. In contrast, supervised LDA amplified subtle chemical differences and achieved clear separation among the three fibers. Feature-loading analysis indicated that the classification was mainly associated with visible range reflectance characteristics, lignin π→π* absorption bands in the 400–450 nm region, and near-infrared O−H and C−H overtone vibrations near 835 nm. Leave-One-Specimen-Out Cross-Validation yielded an overall accuracy of 77.8%, with error-free classification of Kozo (F1 = 1.00) and misclassification limited to the chemically similar Gampi and Mitsumata pair. This proof-of-concept study demonstrates that combining Micro-HSI with chemometric analysis enables non-destructive fiber discrimination while retaining physicochemically interpretable spectral features. The findings also establish a microscopic spectral reference framework for future non-destructive analysis of historical paper materials.
]]>Authors: J. Theo Kloprogge
Understanding the hydration dynamics of montmorillonite clay minerals is critical for predicting their behavior in geotechnical and environmental applications. However, prior ESEM studies have employed separate measurement techniques and lack synchronized multi-scale observations linking microscale aggregate morphology to nanoscale interlayer spacing, with kinetic timescales for clay equilibration remaining unknown. This study employs in situ environmental scanning electron microscopy (ESEM) combined with synchronized X-ray diffraction (XRD) to directly observe and quantify the hydration and dehydration processes of montmorillonite SWy-1 under controlled pressure and temperature conditions on the same sample. ESEM enabled direct visualization of water–clay interactions by precisely controlling chamber pressure (4–5.3 Torr), while synchronized XRD measured basal spacing (d001) changes. Key findings reveal: single water-layer hydration (1W) produces ~19% aggregate swelling and two-layer hydration (2W) yields ~32% swelling; rapid dehydration occurs within 3 min with complete equilibration by 15 min; hydration exhibits steeper pressure dependency (slope = 2.7249) compared to dehydration (slope = 1.6702), indicating thermodynamically driven water uptake but kinetically limited desorption; and water-adsorption isotherms exhibited type-H3 hysteresis. This dual-scale integration establishes practical timescales for clay equilibration and provides critical mechanistic insights for predicting clay behavior in geotechnical engineering and engineered barrier design.
]]>Authors: Luca Bianchini Ciampoli Alessandro Di Benedetto Margherita Fiani Luigi Petti Andrea Benedetto
The fast drainage of surface water from road pavements is essential to ensure both driving safety and adequate infrastructure service life. For close-graded asphalt mixtures, surface runoff relies on sufficient longitudinal and transverse slopes that convey water toward hydraulic drainage devices. However, construction defects, surface distress, or inadequate placement of drainage systems may compromise this process and reduce pavement durability. When water infiltrates beneath the wearing course and saturates the underlying layers, heavy traffic loads can accelerate deterioration through erosion, pumping, interlayer delamination, and subgrade overstress. This work investigates the joint use of Ground Penetrating Radar (GPR) and Mobile Laser Scanning (MLS) to evaluate drainage deficiencies and detect signs of layer delamination in bituminous pavements. A highway section in Salerno (Italy) was selected as a case study due to known hydraulic-related issues. MLS data were used to reconstruct pavement geometry and model surface runoff patterns, while GPR surveys assessed the condition of the bonding between asphalt and base layers. The results revealed ineffective runoff management and identified multiple areas affected by delamination, confirming a relationship between surface drainage behaviour and subsurface damage. These findings highlight the broader potential of the integrated GPR–MLS framework as a scalable and transferable approach for proactive drainage assessment and structural monitoring in pavement management practices.
]]>Authors: Francirley Paz da Silva Robert Saraiva Matos Victor Diogho Heuer de Carvalho Ivan Costa da Silva Carlos Otávio Damas Martins Henrique Duarte da Fonseca Filho
Carburization is a critical degradation mechanism in HP steel alloys used in pyrolysis furnaces, affecting structural integrity and operational reliability. This study evaluates the feasibility of combining ultrasonic A-scan signal processing and Fourier Transform–based spectral descriptors with machine learning to classify four carburization stages in HP steel tube specimens. A total of 160 A-scan waveforms were acquired under controlled laboratory conditions, each containing 2500 sampled points. Frequency-domain features derived from the Discrete Fourier Transform were used as inputs to decision-tree and k-nearest neighbor classifiers. Model performance was assessed using confusion matrices, accuracy, precision, recall, F1-score, and ROC-AUC in a multiclass framework. Ensemble Bagged Trees achieved the highest within-dataset classification accuracy (>99%) under the adopted cross-validation framework, whereas KNN showed lower classification performance despite higher inference speed. The results indicate strong separability among carburization stages under the evaluated acquisition conditions. Given that multiple acquisitions originated from the same tube specimens, the findings should be interpreted as a feasibility-level assessment. Further validation using independent specimens and expanded datasets is required to assess generalization under industrial conditions.
]]>Authors: Agaku Raymond Msughter Jonathan Terseer Ikyumbur Matthew Inalegwu Amanyi Eghwubare Akpoguma Ember Favour Waghbo Patience Uneojo Amaje
This study presents a computational, simulation-based investigation of the dielectric response of human hand tissues, skin, fat, muscle, and bone to radiofrequency (RF) electromagnetic fields emitted by mobile devices. The widespread adoption of handheld devices and the deployment of fifth-generation (5G) networks, including millimetre-wave (mmWave) bands, have intensified concerns regarding localized human exposure to RF radiation, particularly in the hand, which serves as the primary interface during device operation. Using validated dielectric property datasets, numerical simulations were performed across the frequency range of 0.5–40 GHz, employing the Finite-Difference Time-Domain (FDTD) method to solve Maxwell’s equations, with analytical evaluations conducted in Maple-18. A heterogeneous multilayer hand phantom was developed, and simulations were conducted under controlled exposure conditions, including a transmitted power of 1 W, antenna gain of 2 dBi, and incident power density of 5 W/m2, consistent with ICNIRP and NCC safety guidelines. Tissue responses were assessed over a temperature range of 10–40 °C to account for thermal variability. The results demonstrate strong frequency- and temperature-dependent behaviour of dielectric properties, intrinsic impedance, reflection coefficient, attenuation, and specific absorption rate (SAR). At lower frequencies (<1 GHz), RF energy penetrated more deeply with distributed absorption and relatively low SAR values, whereas higher frequencies (3–40 GHz) produced highly localized absorption in superficial tissues, particularly skin and muscle. Increasing temperature led to significant increases in permittivity, conductivity, and SAR, with up to a twofold enhancement observed between 10 °C and 40 °C. These findings confirm that 5G and mmWave exposures result in predominantly surface-confined energy deposition in hand tissues. The study provides a robust computational framework for evaluating hand device electromagnetic interactions and offers quantitative insights relevant to antenna design, exposure compliance assessment, and the development of evidence-based safety guidelines.
]]>Authors: Md. Emon Sardar Muhammad Arifur Rahman Md. Rasheduzzaman Md. Shamsuzzoha Abul Kalam Azad Ayesha Akter Kamrunnahar Ishana Ahmed Parvez Md. Anwarul Abedin Mohammad Kabirul Islam Md. Sagirul Islam Majumder Mehedi Ahmed Ansary Rajib Shaw
Sediment management is defined as the strategic monitoring and control of erosion, transport, and deposition processes to maintain environmental and infrastructural stability. Terrestrial laser scanning (TLS) has emerged as a critical high-precision technology for monitoring sediment dynamics, erosion processes, and geomorphic change detection across diverse environments, including riverine, coastal, watershed, and infrastructure-related landscapes. While the field of TLS technology has seen significant advancements in recent years, including improvements in data accuracy, enhanced operational performance, artificial intelligence (AI), machine learning-based processing, and integration with other remote sensing tools such as unmanned aerial vehicles (UAVs) and satellite light detection and ranging (LiDAR), the study has focused on these developments. These advancements have further extended the application prospects of TLS technology. Despite these advancements, there remains a crucial need to systematically identify global research trends to identify the effectiveness, limitations, and knowledge gaps of TLS in sediment management. The methodological advantages and challenges of TLS applications provide insights into its gradual development role in enhancing sediment monitoring and environmental resilience. The objective of this study is to synthesize the current state of sediment management by conducting a systematic review of 108 peer-reviewed research papers retrieved from academic databases, including Google Scholar, ResearchGate, ScienceDirect, Scopus, and Web of Science, from 28 countries, published between 2000 and 2025. The study will evaluate the effectiveness of TLS methodologies in comparison to conventional techniques and management procedures, following the PRISMA 2020 guidelines. It will examine their capacity to enhance measurement accuracy, reduce error margins, and improve structural guidelines, particularly by advancing TLS technology through the integration of AI and machine learning (ML) algorithms. The findings of the study indicate that TLS and Iterative Closest Point (ICP) techniques can enhance the analysis of 3D models of dam deformation, ensuring improved structural monitoring and safety. The findings offer insights into the evolving role of TLS in sediment monitoring, emphasizing its potential for enhancing environmental management and climate resilience strategies. Furthermore, this review identifies future research directions to optimize TLS applications in sediment management through interdisciplinary approaches.
]]>Authors: Chowdhury Md. Irtiza Bishal Silwal Hossein Taheri
Reliable ultrasonic inspection of welded structures requires a quantitative understanding of how defect morphology and depth influence detectability. In this study, a simulation-based signal-response Probability of Detection (POD) framework is developed to investigate ultrasonic wave interaction with representative planar and volumetric weld defects. Two-dimensional finite-element shear-wave simulations were conducted to model wave propagation and scattering from planar flaws (toe and root cracks) and volumetric flaws (porosity) across defined inspection depth zones. Peak terminal voltage was used as a continuous response metric for regression-based POD analysis. The results demonstrate that defect morphology dominates the influence on ultrasonic detectability. Planar defects produced systematically higher signal responses than volumetric defects of comparable size, resulting in lower characteristic detection limits. The estimated a90 value for planar flaws was 2.96 mm, compared to 5.64 mm for volumetric flaws under identical threshold conditions. Depth-dependent analyses further revealed morphology-specific behavior: planar defects exhibited consistently high detection probabilities across depth zones (POD > 0.98), whereas volumetric defects showed a reduction in detectability with depth, with POD decreasing from approximately 0.32 in shallow zones to 0.16 in deeper regions. The resulting POD trends are interpreted as comparative, trend-based indicators of morphology and depth-dependent ultrasonic detectability under idealized inspection conditions. These findings quantitatively demonstrate how ultrasonic detectability is governed by wave-defect interaction mechanisms associated with defect morphology and inspection depth.
]]>Authors: Marília Marcy Graciela Doz
Efficient structural health monitoring requires not only robust computational strategies but also reliable data acquisition systems capable of capturing representative dynamic responses of real structures. In this study, a continuous dynamic monitoring system composed of accelerometers strategically distributed along the bridge deck provides the foundational data for all subsequent computational analyses. The integrated application of t-Distributed Stochastic Neighbor Embedding (t-SNE) and Learning Vector Quantization (LVQ) is evaluated for the identification of structural damage in the Infante D. Henrique Bridge, located in Porto, Portugal. Data obtained from five years of continuous monitoring were used, with a portion of the identified natural frequencies employed for training and validation of the LVQ algorithm. The robustness of the approach was assessed through artificial modification of data from the second year of monitoring, simulating different damage scenarios. The results demonstrate that the t-SNE–LVQ combination improves discrimination between normal and damaged structural states, achieving identification rates above 70%. The main contribution of this work lies in demonstrating the feasibility and effectiveness of an integrated hardware-to-software machine learning framework applied to real monitoring data, highlighting its potential for structural health monitoring and decision-support systems.
]]>Authors: Jake Pretula Nolan Shaw Elizabeth F. DeCorby Ayden Chen Kyle G. Scheuer Ray G. DeCorby
We report on the non-contact characterization of various plate materials (including aluminum and steel) using a high-pressure, micrometer-scale air jet as a broadband ultrasound source and an optomechanical microphone as a receiver. Through-plate transmission spectra are dominated by zero-group-velocity (ZGV) Lamb modes. We attribute this to the ‘point-like’ nature of both the source and receiver, since ZGV modes are spatially localized and comprise a range of non-normal wave numbers. As is well known, the properties of the ZGV modes, including their frequency and amplitude, are sensitive to thickness variations or the presence of defects. The continuous nature and high acoustic power of the gas jet source enabled us to perform uninterrupted scanning of non-uniform steel plates. Given the ubiquitous and low-cost nature of compressed air systems, our approach might be of interest for the rapid inspection of industrial parts.
]]>Authors: Jarne Van Mulders Jaron Vandenbroucke Merlin Mareschal Bert Cox Emma Tronquo Hans-Peter Marshall Sébastien Lambot Hans Lievens Lieven De Strycker
Current satellite-based active microwave observations lack the temporal resolution needed to accurately capture rapid Earth system dynamics such as soil–plant–atmosphere interactions, rainfall interception, snowfall and rain-on-snow events. Ground-based radar systems can resolve these processes but typically rely on high-end VNAs, limiting their affordability and deployment scale. This work presents a low-cost SFCW radar system built around a compact, SDR-based VNA with an enhanced RF front end supported by remote-access firmware and a cloud-based back end with automatic backup. Calibration experiments and preliminary measurements demonstrate that the system achieves stable performance and is capable of capturing high-temporal-resolution microwave signatures relevant for climate monitoring.
]]>Authors: Mohammad M. Bazrafkan Norbert Hoffmann Marcus Rutner
Detecting structural defects is one of the primary challenges engineers face. Consequently, the development of techniques and methods capable of detecting structural defects has always been critical. It should be emphasized that crack detection is only meaningful if it occurs before the final stages of structural failure. Accordingly, the early identification of structural defects has become a significant research challenge, motivating the development of techniques and diagnostic parameters that can effectively capture and reflect the structure’s nonlinearity or non-uniform behavior. This study aims to provide a more detailed examination of modulation phenomena observed in the measured response using the vibro-acoustic modulation (VAM) method, and propose a new model that simultaneously incorporates all three conventional modulation types (amplitude, frequency, and phase), which may offer a more accurate representation of the response signal behavior. Both theoretical and experimental results clearly confirm that the phase shifts of individual frequency components in the frequency domain vary throughout the lifetime of the tested specimen. This behavior, as anticipated by the proposed model, reveals a strong correlation between phase shifts and modulation indices (MIs). Furthermore, the relative sensitivity analysis indicates that the phase shift is more sensitive than the modulation index (MI), suggesting its strong potential as an indicator for early defect detection in structural components.
]]>Authors: Mário C. O. Spinelli Salomão S. Batista Polyane A. Santos Kenedy Marconi G. Santos Crescencio R. L. Neto Elvio P. Silva Leonardo S. Caires Ronaldo M. Lima Amélia M. Santos Sergio M. O. Tavares Rui A. S. Moreira Décio R. M. Faria Jose A. D. Amado João E. S. Marques Luiz G. M. Souza
The reuse of industrial residues has gained importance due to environmental and public health concerns associated with improper waste disposal. Steel scale (CDA), a by-product of machining and rolling operations, represents a residue with technological potential for incorporation into polymer composites. This study developed a low-cost and sustainable material by reinforcing an orthophthalic polyester matrix with CDA and systematically evaluated its mechanical, thermal, and structural properties. Four formulations were prepared based on the maximum feasible filler loading: R (pure resin), C1 (50% CDA), C2 (100% CDA), and C3 (150% CDA). Composites were manufactured by cold-press molding under a two-ton compressive load. Characterization included tensile, flexural, and impact testing, thermogravimetric analysis (TGA), thermal conductivity, apparent density, liquid absorption, and morphological assessment by scanning electron microscopy (SEM). CDA incorporation reduced tensile and flexural strength but increased elastic modulus, impact toughness, and thermal conductivity. The C3 composite exhibited the highest thermal stability, retaining more than 50% of its initial mass at 500 °C. Density and liquid absorption increased proportionally with filler loading, and SEM revealed heterogeneous microstructures with particle agglomeration, sedimentation, and interfacial gaps, explaining the mechanical and thermal trends. The findings demonstrate the feasibility of producing dense and low-cost polyester composites reinforced with steel scale. The structure–property relationships identified in this study establish a foundation for subsequent investigations focusing on additional functional behaviors of this waste-derived material system.
]]>Authors: Fabio Tosti
The year 2025 marked a defining stage for NDT—Journal of Non-Destructive Testing, consolidating its position as a global platform for advancing non-destructive evaluation science and technology [...]
]]>Authors: Naoki Kamiya Kosuke Ashino Yasuhiro Sakai Yexin Zhou Yoichi Ohyanagi Koji Shibazaki
A few unintended typos were introduced after the proofreading stage, and the authors hence wish to make the following corrections to this paper [...]
]]>Authors: Emiel Hassefras Arno Volker Martin Verweij
Guided wave tomography has proven to be an effective method for detecting pipeline corrosion, providing both location and quantitive estimates of wall thickness loss. However, the limited view geometry of source–receiver pairs on pipes leads to a significantly ill-posed problem. In practical terms, this means that the wall thickness measurements become unreliable, as small errors or noise in the data can result in large inaccuracies in the reconstructed thickness profile. To address the non-uniqueness inherent in Full Waveform Inversion (FWI) for guided wave tomography, we explore a joint inversion framework that combines multiple guided wave modes: specifically A0, S0, and SH1. These modes have different sensitivities to wall thickness variations in pipelines, and by jointly inverting them, we aim to enhance the overall information content available to the inversion process. By deriving statistical measures of solution precision and accuracy through sampling-based analysis, we quantify the reliability of inversion outcomes under different mode-frequency configurations. These measures offer practical guidance for selecting suitable combinations in future experiments, helping to mitigate non-uniqueness without altering the sensor layout. This insight supports more informed system design choices for corrosion monitoring applications.
]]>Authors: Licheng Sha Wenbing Zhang Jiujian Zhou Chengyao Peng Zhenchun Yu
In coal mine production processes, conveyor belts are essential components. They play a crucial role in minimizing the risk of belt failure, enabling unmanned operations in hazardous environments, digitally monitoring production metrics, and facilitating timely information feedback, all of which are vital. This paper provides a systematic review of the fundamental concepts, operational principles, and prevalent algorithms associated with conveyor belt detection technology. It summarizes recent research advancements and current applications in key areas while outlining future trends. The paper addresses the challenges of real-time detection during highspeed operations and the identification of defects in various internal filling materials. It evaluates the feasibility of employing methods such as X-ray detection, magnetic flux leakage detection, ultrasonic detection, radio frequency detection, and terahertz wave detection for high-speed conveyor belt inspection and defect identification in filling materials. Based on a comprehensive analysis, terahertz wave detection technology demonstrates significant potential for advancement in non-destructive testing of conveyor belts, owing to its broad applicability and ability to directly identify the location and size of damage. This review aims to provide technical support for selecting testing methods for steel cord conveyor belts.
]]>Authors: Fabio Tosti
When NDT was launched in 2023, the journal’s vision was to create a dedicated platform for advancing research and innovation in non-destructive testing (NDT) [...]
]]>Authors: Shubhra Shekhar Francisco J. Trujillo Shubhpreet Kaur Kamlesh Prasad
Dielectric characterization offers valuable insights into fruit structure, ripening, and storage stability. However, systematic studies on apples are still limited. This work elucidates the electrical and physicochemical properties of a specific variety of apples, Malus domestica, using Electrochemical Impedance Spectroscopy (EIS), a non-destructive, fast and cost-effective technique, suitable for real-time quality assessments. The apple samples were analyzed over the frequency range of 20 Hz–120 MHz at 25 °C, and impedance data were modeled using equivalent circuits and dielectric relaxation models. Physicochemical analyses confirmed a high moisture content (84%, wwb), pH 4.81, TSS 14.58 °Brix, and acidity 0.64%, which is typical of fresh Red Delicious apples. Impedance spectra revealed semicircular and Warburg elements in Nyquist plots, indicating resistive, capacitive, and diffusive processes. Equivalent circuit fitting with the proposed R-C-Warburg impedance model outperformed (R2 = 0.9946 and RMSE = 6.610) the classical Cole and Double-Shell models. The complex permittivity (ε) represented a frequency-dependent ionic diffusion, space-charge polarization, and dipolar relaxation decay, while electrical modulus analysis highlighted polarization and charge carrier dynamics. The translational hopping of charge carriers was confirmed through AC conductivity following Jonscher’s power law with an exponent of ƞ = 0.627. These findings establish a comprehensive dielectric profile and advanced circuit fitting for biological tissues, highlighting a promising non-invasive approach using EIS for real-time monitoring of fruit quality, with direct applications in post-harvest storage, supply chain management, and non-destructive quality assurance in the food industry.
]]>Authors: Nikos Economou Sobhi Nasir Said Al-Abri Bader Al-Shaqsi Hamdan Hamdan
The Ground Penetrating Radar (GPR) method can image dielectric discontinuities in subsurface structures, which cause the reflection of electromagnetic (EM) waves. These discontinuities are imaged as reflectors in GPR sections, often distorted by diffracted energy. To focus the diffracted energy within the GPR sections, migration is commonly used. The migration velocity of GPR data is a low-wavenumber attribute crucial for effective migration. Obtaining a migration velocity model, typically close to a Root Mean Square (RMS) model, from zero-offset (ZO) data requires analysis of the available diffractions, whose density and (x, t) coverage are random. Thus, the accuracy and efficiency of such a velocity model, whether for migration or interval velocity model estimation, are not guaranteed. An alternative is the multipath summation method, which involves the weighted stacking of constant velocity migrated sections. Each stacked section contributes to the final stack, weighted by a scalar value dependent on the constant velocity value used and its relation to its estimated mean velocity of the section. This method effectively focuses the GPR diffractions in the presence of low heterogeneity. However, when the EM velocity varies dramatically, 2D weights are needed. In this study, with the aid of an Artificial Intelligence (AI) algorithm that detects diffractions and uses their kinematic information, we generate a diffraction velocity model. This model is then used to assign 2D weights for the weighted multipath summation, aiming to focus the scattered energy within the GPR section. We describe this methodology and demonstrate its application in enhancing the lateral continuity of reflections. We compare it with the 1D multipath summation using simulated data and present its application on marble assessment GPR data for imaging cracks and discontinuities in the subsurface structure.
]]>Authors: Sima Das Rishabh Kumar Addya
Skin cancer is one of the most prevalent and potentially lethal cancers worldwide, highlighting the need for accurate and timely diagnosis. Convolutional neural networks (CNNs) have demonstrated strong potential in automating skin lesion classification. In this study, we propose a multi-class classification model using EfficientNet-B0, a lightweight yet powerful CNN architecture, trained on the HAM10000 dermoscopic image dataset. All images were resized to 224 × 224 pixels and normalized using ImageNet statistics to ensure compatibility with the pre-trained network. Data augmentation and preprocessing addressed class imbalance, resulting in a balanced dataset of 7512 images across seven diagnostic categories. The baseline model achieved 77.39% accuracy, which improved to 89.36% with transfer learning by freezing the convolutional base and training only the classification layer. Full network fine-tuning with test-time augmentation increased the accuracy to 96%, and the final model reached 97.15% when combined with Monte Carlo dropout. These results demonstrate EfficientNet-B0’s effectiveness for automated skin lesion classification and its potential as a clinical decision support tool.
]]>Authors: R. Arun Chakravarthy C. Sureshkumar M. Arun M. Bhuvaneswari
The research work presents an artificial intelligence-driven, energy-aware data aggregation and routing protocol for wireless sensor networks (WSNs) with the primary objective of extending overall network lifetime. The proposed scheme leverages reinforcement learning in conjunction with deep Q-networks (DQNs) to adaptively optimize both Cluster Head (CH) selection and routing decisions. An adaptive clustering mechanism is introduced wherein factors such as residual node energy, spatial proximity, and traffic load are jointly considered to elect suitable CHs. This approach mitigates premature energy depletion at individual nodes and promotes balanced energy consumption across the network, thereby enhancing node sustainability. For data forwarding, the routing component employs a DQN-based strategy to dynamically identify energy-efficient transmission paths, ensuring reduced communication overhead and reliable sink connectivity. Performance evaluation, conducted through extensive simulations, utilizes key metrics including network lifetime, total energy consumption, packet delivery ratio (PDR), latency, and load distribution. Comparative analysis with baseline protocols such as LEACH, PEGASIS, and HEED demonstrates that the proposed protocol achieves superior energy efficiency, higher packet delivery reliability, and lower packet losses, while adapting effectively to varying network dynamics. The experimental outcomes highlight the scalability and robustness of the protocol, underscoring its suitability for diverse WSN applications including environmental monitoring, surveillance, and Internet of Things (IoT)-oriented deployments.
]]>Authors: Anwar Ali Sathio Vijay Singh Saeed Anwar Raja Vavekanand
The escalating concerns regarding environmental pollution, particularly industrial water pollution, have necessitated the development of advanced monitoring systems to ensure water resource safety and sustainability. This paper presents an innovative IoT-based Industrial Water Pollution Evaluation System that integrates sensor networks, communication technologies, and data analytics to provide real-time, comprehensive water quality assessment in industrial environments. Our system employs an ESP32 microcontroller connected to four critical sensors (pH, temperature, turbidity, and TDS) to monitor water quality parameters continuously. The collected data is transmitted to a cloud platform and visualized through a dedicated Android application, enabling proactive pollution control measures. Experimental results demonstrate the system’s effectiveness in detecting various pollution scenarios with high accuracy. This solution addresses the limitations of traditional monitoring methods by offering cost-effective, real-time monitoring capabilities that support sustainable water management practices in industrial settings.
]]>Authors: Maha Hadded Thiago Luiz Lara Oliveira Olivier Debono Emilien Bourdon Alan Jean-Marie
Nanoparticle-embedded polymeric materials are an important subject in advanced structural applications due to their advantageous combination of low weight and high mechanical performance. Optical coherence tomography (OCT) is a high-resolution imaging technique that enables subsurface defect visualization, which can be used as one of the methods to reveal defects resulting from decomposition pathways or mechanisms of polymers. Nevertheless, the low contrast of polymeric materials, particularly PEEK-based polymers, does not allow for automatic geometry extraction for analytical input. To address the constraint of weak contrast, localized surface plasmon resonance (LSPR) of plasmonic nanoparticle-reinforced polymer materials has been used as an OCT contrast agent to provide the necessary contrast. The backscattering efficiency of light was also theoretically investigated, based on the Lorenz–Mie theory, with a single spherical nanoparticle embedded in a PEEK matrix as a non-absorptive, isotropic and homogeneous medium. In this study, the cases of a single homogeneous TiO2  nanoparticle and a hybrid TiO2/Au  core/shell nanoparticle configuration were considered separately. An examination of the influence of nanoparticle diameter and gold shell thickness on backscattering efficiencies of these nanostructures was performed. The results indicate that TiO2/Au nanoshells demonstrate superior near-infrared (NIR) light backscattering capabilities at typical OCT operating wavelengths (830–1310 nm). Additionally, the potential of these nanoparticles for application in non-destructive testing-based light backscattering methods was investigated. The findings suggest that TiO2/Au nanoshells have the ability to effectively backscatter near-infrared light in OCT operating central wavelengths, making them suitable to serve as effective NIR contrast-enhancing agents for OCT within the domain of NDT.
]]>Authors: Gopal Tiwari Ram Prasad Sharma Sudipta Chattaraj Abhishek Jangir Benukantha Dash Lal Chand Malav Brijesh Yadav Amrita Daripa
This study presents a terrain-integrated Soil Management Unit (SMU) framework for precision agriculture in semi-arid tropical basaltic soils. Using high resolution (10-ha grid) sampling across 4627 geo-referenced locations and machine learning-enhanced integration of terrain attributes with legacy soil maps, and (3) quantitative validation of intra-SMU homogeneity, 15 SMUs were delineated based on landform, soil depth, texture, and slope. Principal Component Analysis (PCA) revealed SMU11 as the most heterogeneous (68.8%). Geo-statistical analysis revealed structured variability in soil pH (range = 1173 m) and nutrients availability with micronutrient sufficiency following Mn > Fe > Cu > Zn, (Zn deficient in SMU13). Organic carbon strongly correlated with key nutrients (AvK, r = 0.83 and Zn, r = 0.86). This represents the first systematic implementation of terrain-integrated SMU delineation in India’s basaltic landscapes, demonstrating a potential for 20–25% input savings. The spatially explicit fertility-integrated SMU framework provides a robust basis for developing decision support systems aimed at optimizing location-specific nutrient and land management strategies.
]]>Authors: Jose A. Cabello-Mendez Abraham Lopez-Miguel Jose T. Perez-Quiroz Alejandro Moreno-Valdes Jose M. Machorro-Lopez Ilse C. Castillo-Arteaga
Concrete is the most important construction material, and improving its durability properties is a topic in constant development owing to the economic costs that the degradation of concrete implies. Different nanoparticles have been reported to improve concrete durability, although the positive results are not a generality. Among these nanomaterials, graphene oxide stands out as an option for improving concrete properties, such as its compressive strength, which could increase the useful life of concrete infrastructure. This study addresses the effects of graphene oxide on the durability properties of concrete, with the aim of obtaining data on the viability of graphene oxide as an additive in concrete. The incorporation of graphene oxide into concrete was carried out through graphene oxide suspensions that were incorporated into concrete mixtures with a high water/cement ratio. The characterization of concrete was done using non-destructive testing such as ultrasonic pulse velocity, electrical resistivity, porosity, capillary absorption, chloride ion permeability, and other characterization methods such as compressive strength, XPS, SEM, and EDS. Together, these tests provided an overview of the concrete durability properties that are improved, affected, or unchanged by the presence of graphene oxide. In this study, a chemical analysis was also carried out on concrete modified with graphene oxide. The results show that graphene oxide improves the compressive strength of concrete, but the effect on durability properties is negligible; however, there are indications that, in combination with other additives, improvements can be achieved, so it is advisable to continue with these studies.
]]>Authors: Angeliki Armeni
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.
]]>Authors: Guilherme Pina Cardim Thiago de Souza Duarte Henrique Pina Cardim Wallace Casaca Rogério Galante Negri Flávio Camargo Cabrera Renivaldo José dos Santos Erivaldo Antônio da Silva Mauricio Araujo Dias
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.
]]>Authors: Usman Sa’i Ibrahim Kamorudeen Aleem Tajul Ariffin Musa Terwase Tosin Youngu Yusuf Yakubu Obadaki Wan Anom Wan Aris Kelvin Tang Kang Wee
Climate change monitoring and analysis is a critical task that involves the consideration of both spatial and temporal dimensions. Theimproved spatial distribution of the global navigation satellite system (GNSS) ground-based Continuous Operating Reference (COR) stations can lead to enhanced results when coupled with a continuous flow of data over time. In Africa, a significant number of COR stations do not operate continuously and lack collocation with meteorological sensors essential for climate studies. Consequently, Africa faces challenges related to inadequate spatial distribution and temporal data flow from GNSS ground-based stations, impacting climate change monitoring and analysis. This research delves into the pattern of GNSS radio occultation (RO) data across Africa, addressing the limitations of the GNSS ground-based data for climate change research. The spatial analysis employed Ripley’s F-, G-, K-, and L-functions, along with calculations of nearest neighbour and Kernel density. The analysis yielded a Moran’s p-value of 0.001 and a Moran’s I-value approaching 1.0. For temporal analysis, the study investigated the data availability period of selected GNSS RO missions. Additionally, it examined seasonal temperature variations from May 2001 to May 2023, showcasing alignment with findings from other researchers worldwide. Hence, this study suggests the utilisation of GNSS RO missions/campaigns like METOP and COSMIC owing to their superior spatial and temporal resolution.
]]>Authors: Siuli Dutta Ashis K. Panda Rajat K. Roy
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.
]]>Authors: Varsha Samuel Daniel Spence Liam Farmer Simon Godber Keith Rogers Anthony Dicken
New methods for real-time materials phase identification based upon focal construct tomography (FCT) have been examined. Such quantitative assessment has significant potential in sectors where in-line analysis is required, including screening within aviation security. As a recent component of work programs developing FCT, its capability for accurate, quantitative analysis has been assessed for the first time. Diffraction signatures from mixed-phase materials were acquired from an energy-dispersive FCT system running under normal operational conditions. A calibration curve was constructed from the spectra and subsequently employed to assess the composition of ‘blind’ samples. The results demonstrated that this approach was able to precisely predict the polymorphic phase composition of samples to ±5 wt%. Conclusions: The potential impact of these findings is significant and will enable applications of FCT beyond those requiring a phase identification to those necessitating quantification, such as counterfeit medicines, pharmaceutical quality assurance, aging of explosives, and cement production.
]]>Authors: Guillermo Huanes-Alvan Himanshu Sahasrabudhe Sunil Kishore Chakrapani
This article explores the use of ultrasonic nondestructive evaluation for qualification of laser-DED IN718 samples. The main goal of this article is to identify potential ultrasonic parameters which have highest sensitivity to microstructral changes that result from fabrication of DED samples. The ultrasonic qualification parameters were extracted from ultrasonic testing including velocity and attenuation measurement, and C-Scan imaging. These measurements were further used to extract parameters that quantify the anisotropy, microstructural heterogeneity, and grain scattering. Two laser-DED IN718 samples fabricated with slightly different processing parameters were evaluated to observe the influence of the laser power and scan speed on the qualification parameters. The identified qualification parameters were compared for these two samples, along with a hot-rolled sample that was also used as reference. The results suggest that the anisotropy, attenuation, and heterogeneity were highest in the DED samples compared to the reference sample. The identified qualification parameters seem to capture these changes, suggesting they could be potentially used for qualification of AM parts.
]]>Authors: Rakib Ahammed Diptho Sarnali Basak
Skin diseases represent a major worldwide health hazard affecting millions of people yearly and substantially compromising healthcare systems. Particularly in areas where dermatologists are scarce, standard diagnostic techniques, which mostly rely on visual inspection and clinical experience, are frequently subjective, time-consuming, and prone to mistakes. This investigation undertakes a comparative analysis of four state-of-the-art deep learning architectures, YOLO11, YOLOv8, VGG16, and ResNet50, in the context of skin disease identification. This study evaluates the performance of these models using pivotal metrics, building upon the foundation of the YOLO paradigm, which revolutionized spatial attention and multi-scale representation. A properly selected collection of 900 high-quality dermatological images with nine disease categories was used for investigation. Robustness and generalizability were guaranteed by using data augmentation and hyperparameter adjustment. By varying benchmark models in balancing accuracy and recall while limiting false positives and false negatives, YOLO11 obtained a test accuracy of 80.72%, precision of 88.7%, recall of 86.7%, and an F1 score of 87.0%. The expedition performance of YOLO11 signifies a promising trajectory in the development of highly accurate skin disease detection models. Our analysis not only highlights the strengths and weaknesses of the model but also underscores the rapid development of deep learning techniques in medical imaging.
]]>Authors: Shanmugam Vijayakumar Palanisamy Shanmugapriya Pasoubady Saravanane Thanakkan Ramesh Varunseelan Murugaiyan Selvaraj Ilakkiya
Weeds cause significant yield and economic losses by competing with crops and increasing production costs. Compounding these challenges are labor shortages, herbicide resistance, and environmental pollution, making weed management increasingly difficult. In response, precision weed control (PWC) technologies, such as robots and unmanned aerial vehicles (UAVs), have emerged as innovative solutions. These tools offer farmers high precision (±1 cm spatial accuracy), enabling efficient and sustainable weed management. Herbicide spraying robots, mechanical weeding robots, and laser-based weeders are deployed on large-scale farms in developed countries. Similarly, UAVs are gaining popularity in many countries, particularly in Asia, for weed monitoring and herbicide application. Despite advancements in robotic and UAV weed control, their large-scale adoption remains limited. The reasons for this slow uptake and the barriers to widespread implementation are not fully understood. To address this knowledge gap, our review analyzes 155 articles and provides a comprehensive understanding of PWC challenges and needed interventions for scaling. This review revealed that AI-driven weed mapping in robots and UAVs struggles with data (quality, diversity, bias) and technical (computation, deployment, cost) barriers. Improved data (collection, processing, synthesis, bias mitigation) and efficient, affordable technology (edge/hybrid computing, lightweight algorithms, centralized computing resources, energy-efficient hardware) are required to improve AI-driven weed mapping adoption. Specifically, robotic weed control adoption is hindered by challenges in weed recognition, navigation complexity, limited battery life, data management (connectivity), fragmented farms, high costs, and limited digital literacy. Scaling requires advancements in weed detection and energy efficiency, development of affordable robots with shared service models, enhanced farmer training, improved rural connectivity, and precise engineering solutions. Similarly, UAV adoption in agriculture faces hurdles such as regulations (permits), limited payload and battery life, weather dependency, spray drift, sensor accuracy, lack of skilled operators, high initial and operational costs, and absence of standardized protocol. Scaling requires financing (subsidies, loans), favorable regulations (streamlined permits, online training), infrastructure development (service providers, hiring centers), technological innovation (interchangeable sensors, multipurpose UAVs), and capacity building (farmer training programs, awareness initiatives).
]]>Authors: Aleksey Mironov Pavel Doronkin Aleksejs Safonovs
One of the most sensitive parts of a wind turbine to environmental influences are the rotating blades. Today, there are many technologies available to assess blade condition, but they all need to be developed to become more cost-effective and more sensitive to fault detection. The algorithms and methods of the modal passport discussed in this paper propose a non-destructive technique already used for helicopter blade condition monitoring and diagnostics. This technique requires adaptation to wind turbine blades because they have larger dimensions, other materials and design, and operate under other conditions. To provide this adaptation, computational and experimental data on the modal properties of the blades must be obtained. The first stage of the study is planned to be performed on a scale model on stationary and rotating test rigs. At this stage of the study, algorithms and methods for the formation of a roadmap to develop a modal passport for a series of composite models of a wind turbine blade are considered. The initial stage of modal passport development included FE modeling of the blade model, calculation of modal parameters, fabricating the blades, and preparing the test equipment. Quantitative assessment of modal tests volume made it possible to plan the step-by-step execution of the roadmap for development and experimental application of the modal passport of wind turbine blade models.
]]>Authors: Gidewon G. Tekeste António A. Correia Aníbal G. Costa
Hybrid simulation integrates numerical and experimental techniques to analyze structural responses under static and dynamic loads. It physically tests components that are not fully characterized while modeling the rest of the structure numerically. Over the past two decades, hybrid testing platforms have become increasingly modular and versatile. This paper presents the development of a robust hybrid testing software framework at the National Laboratory for Civil Engineering (LNEC), Portugal, and evaluates the efficiency of its algorithms. The framework features a LabVIEW-based control and interface application that exchanges data with OpenSees via the OpenFresco middleware using a TCP/IP protocol. Designed for slow to real-time hybrid testing, it employs a predictor–corrector algorithm for motion control, enhanced by an adaptive time series (ATS)-based error tracking and delay compensation algorithm. Its modular design facilitates the integration of new simulation tools. The framework was first assessed through simulated hybrid tests, followed by validation via a hybrid test on a two-bay, one-story steel moment-resisting frame, where one exterior column was physically tested. The results emphasized the importance of the accurate system identification of the physical substructure and the precise calibration of the actuator control and delay compensation algorithms.
]]>Authors: Carl Lee Tolbert
Nondestructive testing (NDT) has a crucial role in ensuring the reliability and safety of industrial systems. However, traditional methods typically rely on external sensors, which can lead to increased costs and added complexity. The current study examined an alternative approach using variable-frequency drive (VFD) data for real-time fault detection and predictive maintenance. Most VFDs continuously monitor essential parameters such as motor speed, torque, efficiency, and power consumption, facilitating sensorless condition monitoring that helps detect early-stage motor and apparatus faults without additional hardware. To improve diagnostic capabilities, calculated metrics such as apparent power, efficiency, torque, and energy consumption can deliver more profound insights into system performance, assisting in identifying potential failure patterns. A Python-based data acquisition and visualization system was developed and implemented as an example of a potential solution, enabling centralized monitoring, anomaly detection, and historical data analysis. Future advancements in artificial intelligence and machine learning could further refine automated fault detection by utilizing historical VFD data to predict system failures accurately. By integrating VFD-based diagnostics into NDT, industries can develop scalable, cost-effective, intelligent testing and maintenance solutions that improve reliability and asset management in modern systems.
]]>Authors: Obinna Okolie Nadimul Haque Faisal Harvey Jamieson Arindam Mukherji James Njuguna
The thermoplastic composite pipe (TCP) manufacturing process introduces defects that impact performance, such as voids, misalignment, and delamination. Consequently, there is an increasing demand for effective non-destructive testing (NDT) techniques to assess the influence of these manufacturing defects on TCP. The objective is to identify and quantify internal defects at a microscale, thereby improving quality control. A combination of methods, including NDT, has been employed to achieve this goal. The density method is used to determine the void volume fraction. Microscopy and void analysis are performed on pristine samples using optical micrography and scanning electron microscopy (SEM), while advanced techniques like X-ray computer tomography (XCT) and ultrasonic inspections are also applied. The interlayer between the reinforced and inner layers showed good consolidation, though a discontinuity was noted. Microscopy results confirmed solid wall construction, with SEM aligning with the XY axis slice, showing predominant fibre orientation around ±45° and ±90°, and deducing the placement orientation to be ±60°. Comparing immersion, 2D microscopy, and XCT methods provided a comparative approach, even though they could not yield precise void content values. The analysis revealed a void content range of 0–2.2%, with good agreement between microscopy and Archimedes’ methods. Based on XCT and microscopy results, an increase in void diameter at constant volume increases elongation and reduces sphericity. Both methods also indicated that most voids constitute a minority of the total void fraction. To mitigate manufacturing defects, understanding the material’s processing window is essential, which can be achieved through comprehensive material characterization of TCP materials.
]]>Authors: Philip Reeder Harry Jol
The non-destructive geophysical testing method ground penetrating radar (GPR), along with satellite image and air photo assessment, a review of the existing literature sources, and Holocaust survivor testimony, was used to document the location of potential mass graves in Alytus, Lithuania. In World War II, six million Jews were murdered, as were as many as five million other victims of Nazi Germany’s orchestrated persecution. In the summer of 1941, 8030 Jews (4.70 percent of Lithuania’s Jewish population) lived in Alytus County, where the town of Alytus is located. It is estimated that over 8000 Jews were murdered in Alytus County, including nearly the entire Jewish population of the town of Alytus. The murder of Jews from Alytus County accounts for approximately 4.2% of the total number of Lithuanian Jews killed in the Holocaust. Survivor testimony indicates that several thousand Jews from both the town and county were murdered and buried in the Vidzgiris Forest about 1000 m from the town center. In 2022, field reconnaissance at locations in the forest, which appeared to be disturbed in a 1944 German Luftwaffe air photograph, indicated that these disturbances were associated with natural geomorphic processes and not the Holocaust. Analysis of GPR data that was collected using a pulseEKKO Pro 500-megahertz groundpenetrating radar (GPR) system in 2022 in the vicinity of monuments erected in the forest to memorialize mass graves indicates that no mass graves were directly associated with these monuments. The 1944 air photograph contained two roads that traversed through and abruptly ended in the forest, which was the impetus for detailed field reconnaissance in that area. A segment of a 150 m long linear surface feature found in the forest was assessed using GPR, and based on the profile that was generated, it was determined that this feature is possibly a segment of a much more extensive mass grave. Testimony of a Holocaust survivor stated that as many as three burial trenches exist in this portion of the forest. Additional research using non-destructive GPR technology, air photograph and satellite image assessment, and the existing literature and testimony-based data are required for the Vidzgiris Forest to better define these and other potential mass graves and other Holocaust-related features.
]]>Authors: Samuel J. I. Forster Daniel Conniffe Anthony J. Peyton Frank J. W. Podd Nigel Davidson Joshua B. Elliott
Radar polarimetric imaging for non-destructive testing is a powerful and flexible tool that can be used to enhance the detection of internal structures. In this study, reinforced autoclaved aerated concrete (RAAC) is measured using a polarimetric system in three different acquisition modes—two downward-looking and one sideways-looking configurations, each at a different height. Each acquisition mode is compared and new polarisation states are created using the principle of polarisation synthesis. Images of the internal structures are created using a 3D imaging algorithm, which are used for the analysis. The comparison between acquisition modes demonstrates that using a higher lift-off and polarisation synthesis could offer more flexible operation in the field, allowing the use of handheld detectors and drone-based systems for inaccessible areas. Additionally, the sideways-looking data captured both horizontal and vertical reinforcement and were detected within a single polarisation channel; this configuration also has reduced clutter from the air–concrete boundary, providing a viable option for single polarisation systems.
]]>Authors: Thiago Luiz Lara Oliveira Maha Hadded Saliha Mimouni Renata Brandelli Schaan
This review examines the essential application of non-destructive testing (NDT) techniques in assessing the integrity and damage of composite materials used in aerospace engineering, focusing on polymer matrix composites (PMCs), metal matrix composites (MMCs), and ceramic matrix composites (CMCs). As these materials increasingly replace traditional metallic and alloy components due to their advantageous properties, such as light weight, high strength, and corrosion resistance, ensuring their structural integrity becomes paramount. Here, various NDT techniques were described in detail, including ultrasonic, radiographic, and acoustic emission, among others, highlighting their significance in identifying and evaluating damages that are often invisible, yet critical, to parts safety. It stresses the need for innovation in NDT technologies to keep pace with the evolving complexity of composite materials and their applications. The review underscores the ongoing challenges and developments in NDT, advocating for enhanced techniques that provide accurate, reliable, and timely assessments to ensure the safety and durability of aerospace components. This comprehensive analysis not only illustrates current capabilities but also directs future research pathways for improving NDT methodologies in aerospace material engineering.
]]>Authors: Sharath Subadra Roy Skaria Andrea Hasselmann Eduard Mayer Shahram Sheikhi
The calibration of a universal testing machine (UTM) verifies the accuracy of the system instruments responsible for obtaining force and displacement measurements. This process involves comparing the instrument to equipment that has already been calibrated to a known traceable standard. The limit of accuracy is then certified and the traceability of the measurements is determined. There are several internationally recognized standards that are used to calibrate the cross-head speed and displacement (ASTM E2658 and E2309, respectively), strain and load rate (ASTM E2309), measurement of tension, compression (ASTM E4) and dynamic force (ASTM E467). The current study aims to monitor the calibration status of UTMs through the implementation of acoustic methods. A methodology is developed whereby a reference sample is initially identified with suitable material properties, enabling it to be used continuously. The sample is used simultaneously with acoustic instruments to check its natural frequencies, which enables the monitoring of the UTM calibration status. An algorithm is developed that enables the user to interact with the system, thus forming an interface and helping the user to check the calibration status of the equipment. The entire system is validated to check if the equipment and the inbuilt algorithm can predict the calibration status of the machine. It was found that the geometric constraints imposed on the sample influence the output from the algorithm, and hence correct values should be fed to the system. Our sample never lost its elastic characteristics through continuous use, demonstrating that it can be used to continuously monitor the machine’s status.
]]>Authors: Lawrence Yule Nicholas Harris Martyn Hill Bahareh Zaghari
Temperature has a significant impact on the operational lifetime of electronic components, as excessive heat can lead to accelerated degradation and ultimately failure. In safety-critical applications, it is important that real-time monitoring is employed to reduce the risk of system failures and maintain the safety, reliability, and integrity of the connected systems. In the case of printed circuit boards (PCBs), it is often not feasible to install enough sensors to adequately cover all of the temperature sensitive components. In this study, we present a novel method for the temperature monitoring of PCBs using ultrasonic guided waves and machine learning techniques. Our approach utilizes a small number of low-cost, unobtrusive piezoelectric wafer active sensors (PWAS) sensors for propagating ultrasonic guided waves across a PCB. Through interaction with board features, the temperature of components can be predicted using multi-output regression algorithms. Our technique has been applied to three different PCBs, each with five hotspot positions, achieving an RMSE of <3.5 °C and R2 > 0.95 in all three cases.
]]>Authors: Fabio Tosti
After nearly two years of consistent activities, the journal NDT (ISSN 2813-477X) [...]
]]>Authors: Abraham Lopez-Miguel Jose A. Cabello-Mendez Alejandro Moreno-Valdes Jose T. Perez-Quiroz Jose M. Machorro-Lopez
Concrete is currently the most used construction material, mainly due to its mechanical strength, chemical stability, and low cost. This material is affected by wear processes caused by the environment, which lead to a reduction in the useful life of the infrastructure in the long term. These wear processes can cause cracks, corrosion of reinforcing steel, loss of load capacity, and loss of concrete section, among other problems. Considering the above, it is necessary to carry out durability studies on concrete to determine the integrity conditions in which the infrastructure is found, the reasons for its deterioration, the environmental factors that affect it, and its useful life under these conditions, and develop restoration or protection plans. Generally, the durability studies include non-destructive testing such as ultrasonic pulse velocity, electrical resistivity, porosity measurement, and capillary absorption rate. These techniques make it possible to characterize the concrete and obtain information such as the total volume of pores, susceptibility to corrosion of the reinforcing steel, decrease in mechanical resistance, cracks, presence of humidity, and aggressive ions inside the concrete. In this work, two durability studies are presented with non-destructive tests carried out on active piers that are 20 and 40 years old. These are located in coastal areas in southern Mexico on the Gulf of Mexico side, with 80% average annual relative humidity, temperatures above 33 °C on average, high concentrations of salts, load handling, vibrations, flora and fauna typical of the marine ecosystem, etc. The results obtained reveal important information about the current state of the piers and the damage caused by the environment over time. This information allowed us to make decisions on preventive actions and develop appropriate and specific restoration projects for each pier.
]]>Authors: Leith Bounenni Mohamed Arbane Clemente Ibarra-Castanedo Yacine Yaddaden Sreedhar Unnikrishnakurup Andrew Ngo Chun Yong Xavier Maldague
Detecting defects on aerospace surfaces is critical to ensure safety and maintain the integrity of aircraft structures. Traditional methods often need more precision and efficiency for effective defect detection. This paper proposes an innovative approach that leverages deep learning and infrared imaging techniques to detect defects with high precision. The core contribution of our work lies in accurately detecting the size and depth of defects. Our method involves segmenting the size of the defect and calculating its centre to determine its depth. We achieve a more comprehensive and precise assessment of defects by integrating deep learning with infrared imaging based on the U-net model for segmentation and the CNN model for classification. The proposed model was rigorously tested on both a simulation dataset and an experimental dataset, demonstrating its robustness and effectiveness in accurately identifying and assessing defects on aerospace surfaces. The results indicate significant improvements in detection accuracy and computational efficiency, showing advancements over state-of-the-art methods and paving the way for enhanced maintenance protocols in the aerospace industry.
]]>Authors: Anthonin Demarbaix Enrique Juste Tim Verlaine Ilario Strazzeri Julien Quinten Arnaud Notebaert
Additive manufacturing of advanced materials has become widespread, encompassing a range of materials including thermoplastics, metals, and ceramics. For the ceramics, the complete production process typically involves indirect additive manufacturing, where the green ceramic part undergoes debinding and sintering to achieve its final mechanical and thermal properties. To avoid unnecessary energy-intensive steps, it is crucial to assess the internal integrity of the ceramic in its green stage. This study aims to investigate the use of active thermography for defect detection. The approach is to examine detectability using two benchmarks: the first focuses on the detectability threshold, and the second on typical defects encountered in 3D printing. For the first benchmark, reflection and transmission modes are tested with and without a camera angle to minimize reflection. The second benchmark will then be assessed using the most effective configurations identified. All defects larger than 1.2 mm were detectable across the benchmarks. The method can successfully detect defects, with transmission mode being more suitable since it does not require a camera angle adjustment to avoid reflections. However, the method struggles to detect typical 3D-printing defects because the minimum defect size is 0.6 mm, which is the size of the nozzle.
]]>Authors: Naoki Kamiya Kosuke Ashino Yasuhiro Sakai Yexin Zhou Yoichi Ohyanagi Koji Shibazaki
Over the years, research in the field of cultural heritage preservation and document analysis has exponentially grown. In this study, we propose an advanced approach for non-destructive estimation of paper fibers using macro images. Expanding on studies that implemented EfficientNet-B0, we explore the effectiveness of six other deep learning networks, including DenseNet-201, DarkNet-53, Inception-v3, Xception, Inception-ResNet-v2, and NASNet-Large, in conjunction with enlarged patch sizes. We experimentally classified three types of paper fibers, namely, kozo, mitsumata, and gampi. During the experiments, patch sizes of 500, 750, and 1000 pixels were evaluated and their impact on classification accuracy was analyzed. The experiments demonstrated that Inception-ResNet-v2 with 1000-pixel patches achieved the highest patch classification accuracy of 82.7%, whereas Xception with 750-pixel patches exhibited the best macro-image-based fiber estimation performance at 84.9%. Additionally, we assessed the efficacy of the method for images containing text, observing consistent improvements in the case of larger patch sizes. However, limitations exist in background patch availability for text-heavy images. This comprehensive evaluation of network architectures and patch sizes can significantly advance the field of non-destructive paper analysis, offering valuable insights into future developments in historical document examination and conservation science.
]]>Authors: Konstantina Georgouli Christina Plati Andreas Loizos
Non-destructive testing (NDT) systems are essential tools and are widely used for assessing the condition and structural integrity of pavement structures without causing any damage. They are cost-effective, provide comprehensive data, and are time efficient. The bearing capacity and structural condition of a flexible pavement depends on several interrelated factors, with asphalt layers stiffness being dominant. Since asphalt mix is a viscoelastic material, its performance can be fully captured by the dynamic modulus master curve. However, in terms of evaluating an in-service pavement, although a dynamic load is applied and the time history of deflections is recorded during testing of FWD, only the peak deflection is considered in the analysis. Therefore, the modulus of stiffness estimated by backcalculation is the modulus of elasticity. While several methods have been introduced for the determination of the field dynamic modulus master curve, the MEPDG approach provides significant advantages in terms of transparency and robustness. This study focuses on evaluating the methodology’s accuracy through an experimental study. The data analysis and validation process showed that routine measurements with the FWD and GPR, within the framework of a pavement monitoring system, can provide valuable input parameters for the evaluation of in-service pavements.
]]>Authors: Alan Nagaya Oscar G. de Lucio Soledad Ortiz Ruiz Eunice Uc González Carlos Peraza Lope Wilberth Cruz Alvarado
Archaeological pottery X-radiography is mainly used for two applications: fabric characterization and identification of forming techniques. Both applications require imaging of tempering materials and other additives. With digital X-radiography, it is easy to enhance the image to compute and characterize these materials. In this study, a combination of ImageJ plug-ins such as “threshold”, “analyze particles”, and “fit polynomial” were used to describe tempering materials of a set composed of archaeological pottery sherds. It was found that two different types of tempering materials were used. The first type was characterized by a grain size of less than 0.5 mm and no well-formed particles. In contrast, the second group had a grain size larger than 0.5 mm and well-formed particles.
]]>Authors: Ishant Singh Vanita Arora Prabhu Babu Ravibabu Mulaveesala
In the field of thermal non-destructive testing and evaluation (TNDT&E), active thermography gained popularity due to its fast wide-area monitoring and remote inspection capability to assess materials without compromising their future usability. Among the various active thermographic methods, pulse compression-favorable frequency-modulated thermal wave imaging stands out for its enhanced detectability and depth resolution. In this study, an experimental investigation has been carried out on a hardened steel sample used in the ship building industry with a flat-bottom-hole-simulated defect using the frequency-modulated thermal wave imaging (FMTWI) technique. The defect detection capabilities of FMTWI have been investigated from various statistical post-processing approaches and compared by taking the signal-to-noise ratio (SNR) as a figure of merit. Among various adopted statistical post-processing techniques, pulse compression has been carried out using different methods, namely the offset removal with polynomial curve fitting and principal component analysis (PCA), which is an unsupervised learning approach for data reduction and offset removal with median centering for data standardization. The performance of these techniques was assessed through experimental investigations on hardened steel specimens used in ship building to provide valuable insights into their effectiveness in defect detection capabilities.
]]>Authors: Christina Plati Angeliki Armeni Andreas Loizos
The successful management of road pavement maintenance requires the existence of suitable monitoring procedures for assessing pavement condition. A powerful tool for this is the use of non-destructive testing technologies. Non-destructive testing (NDT) aims to support the monitoring of pavement condition, as it enables constant and rapid collection of in situ data. Analyzing NDT data can result in the development of useful indexes that can be related to trigger values (criteria) to define pavement condition. This information can be used to assess the “health” of the pavement to decide whether intervention is required. However, to effectively support the implementation of pavement management measures, it is sometimes necessary to implement a pavement monitoring and assessment framework that can be adapted by road authorities on a case-by-case basis. To this end, this study addresses the development of a pavement monitoring and assessment procedure by integrating different NDT technologies to collect and evaluate data. The procedure, referred to as Integrated Testing and Evaluation (ITE), is proposed as an algorithm to find optimal strategies for prioritizing potential pavement interventions, considering the budget constraints for the required investigations.
]]>Authors: Kevin K. McCully Sarah N. Stoddard Mary Ann Reynolds Terence E. Ryan
This study characterized the level of oxidative metabolism in skeletal muscle during whole-body activity as a percentage of the muscle’s maximum oxidative rate (mVO2max) using near-infrared spectroscopy (NIRS). Ten healthy participants completed a progressive work test and whole-body walking and lunge exercises, while oxygen saturation was collected from the vastus lateralis muscle using near-infrared spectroscopy (NIRS). Muscle oxygen consumption (mVO2) was determined using arterial occlusions following each exercise. mVO2max was extrapolated from the mVO2 values determined from the progressive exercise test. mVO2max was 11.3 ± 3.3%/s on day one and 12.0 ± 2.9%/s on day two (p = 0.07). mVO2max had similar variation (ICC = 0.95, CV = 6.4%) to NIRS measures of oxidative metabolism. There was a progressive increase in mVO2 with walking at 3.2 Km/h, 4.8 km/h, 6.4 Km/h, and with lunges (15.8 ± 6.6%, 20.5 ± 7.2%, 26.0 ± 6.6%, and 57.4 ± 15.4% of mVO2max, respectively). Lunges showed a high reliability (ICC = 0.81, CV = 10.2%). Muscle oxidative metabolism in response to whole-body exercise can be reproducibly measured with arterial occlusions and NIRS. This method may be used to further research on mitochondrial activation within a single muscle during whole-body exercise.
]]>Authors: Sanja Seratlic Bikash Guha Sean Moore
The quest for reliable techniques to predict Cheddar cheese maturity has gained momentum to ensure quality and consistency in large-scale production. Given the complexity of cheese ripening and the industry’s need for fast and reliable evaluation methods, this review addresses the challenge by scrutinising the application of spectroscopic techniques such as Fourier transform infrared (FT-IR), near-infrared (NIR), and nuclear magnetic resonance (NMR). These methods are evaluated for their noninvasive and rapid on-site analysis capabilities, which are essential for ensuring quality in cheese production. This review synthesises current research findings, discusses the potential and limitations of each technique, and highlights future research directions. Overall, NIR spectroscopy emerges as the most promising, offering quick, nondestructive assessments and reasonably accurate compositional predictions, crucial for real-time maturation monitoring. It provides rapid results within minutes, making it significantly faster than FT-IR and NMR. While FT-IR also offers high accuracy, it typically requires longer analysis times due to extensive calibration and can be sensitive to sample conditions, while NMR, although highly accurate, involves complex and time-consuming procedures. Nonetheless, further studies are necessary to refine these spectroscopic techniques, enhance their predictive accuracy, and deepen the understanding of the correlations between chemical attributes and sensory qualities in Cheddar cheese.
]]>Authors: Bata Hena Gabriel Ramos Clemente Ibarra-Castanedo Xavier Maldague
Process automation utilizes specialized technology and equipment to automate and enhance production processes, leading to higher manufacturing efficiency, higher productivity, and cost savings. The aluminum die casting industry has significantly gained from the implementation of process automation solutions in manufacturing, serving safety-critical sectors such as automotive and aerospace industries. However, this method of component fabrication is very susceptible to generating manufacturing flaws, hence necessitating adequate non-destructive testing (NDT) to ascertain the fitness for use of such components. Machine learning has taken the center stage in recent years as a tool for developing automated solutions for detecting and classifying flaws in digital X-ray radiography. These machine learning-based solutions have increasingly been developed and deployed for component inspection, to keep pace with the high production throughput in manufacturing industries. This work focuses on the development of a defect grading algorithm that assesses detected flaws to ascertain if they constitute a defect that could render a component unfit for use. Guided by ASTM 2973-15; Standard Digital Reference Images for Inspection of Aluminum and Magnesium Die Castings, a grading pipeline utilizing K-D (k-dimensional) trees was developed to effectively structure detected flaws, enabling the system to make decisions based on acceptable grading terms. This solution is dynamic in terms of its conformity to different grading criteria and offers the possibility to achieve automated decision making (Accept/Reject) in digital X-ray radiography applications.
]]>Authors: Thomas Schumacher
To optimally preserve and manage our civil structures, we need to have accurate information about their (1) geometry and dimensions, (2) boundary conditions, (3) material properties, and (4) structural conditions. The objective of this article is to show how imaging and image fusion using non-destructive testing (NDT) measurements can support structural engineers in performing accurate structural evaluations. The proposed methodology involves imaging using synthetic aperture focusing technique (SAFT)-based image reconstruction from ground penetrating radar (GPR) as well as ultrasonic echo array (UEA) measurements taken on multiple surfaces of a structural member. The created images can be combined using image fusion to produce a digital cross-section of the member. The feasibility of this approach is demonstrated using a case study of a prestressed concrete bridge that required a bridge load rating (BLR) but where no as-built plans were available. Imaging and image fusion enabled the creation of a detailed cross-section, allowing for confirmation of the number and location of prestressing strands and the location and size of internal voids. This information allowed the structural engineer of record (SER) to perform a traditional bridge load rating (BLR), ultimately avoiding load restrictions being imposed on the bridge. The proposed methodology not only provides useful information for structural evaluations, but also represents a basis upon which the digitalization of our infrastructure can be achieved.
]]>Authors: Amartya Paul Hemant Kumari Rinaldo Snaitang Pradeep Kumar Gautam Shubhankar Majumdar
This research investigates the characterization of bituminous mixes utilizing microwave imaging and non-destructive testing. We studied the electromagnetic characteristics of various samples, including bituminous concrete (BC) and open-grade friction course (OGFC) samples. A novel ring filter with log-periodic feedlines, designed on the RT/Duroid 5880 substrate, was utilized within the frequency range of 0.3–0.7 GHz. The samples were assessed using average attenuation and group delay measures, which detailed clear electromagnetic characteristics. The samples’ flow value and specific gravity were correlated to these parameters. The calculated flow value and specific gravity (using the filter) and measured flow value and specific gravity (using the conventional method) coincided well. The filter could predict the parameters of the samples with a high accuracy of roughly 99.8% for the flow value and specific gravity, whereas the OGFC sample displayed an accuracy of 99.7%, correspondingly, as shown in high R2 values. This demonstrates that the filter can precisely measure the parameters required for studying the interaction between the binder and aggregate in bituminous mixes without being invasive. The findings indicate a significant disparity between OGFC and BC samples in their responses to electromagnetic fields and their characteristics. This demonstrates the high sensitivity and significant value of microwave techniques in the study of bitumen and the construction of roadways.
]]>Authors: Marius W. Schäfer Sarah C. L. Fischer
The increasing complexity of material systems requires an extension of conventional non-destructive evaluation methods such as ultrasonic testing. Many publications have worked on extending simulation models to cover novel aspects of ultrasonic transducers, but they do not cover all components of the system. This paper presents a physically motivated, modular model that describes the complete signal flow with the aim of providing a platform for optimizing ultrasonic testing systems from individual components to the whole system level. For this purpose, the ultrasonic testing system is divided into modules, which are described by models. The modules are each parameterized by physical parameters, characteristics of real components as provided by datasheets, or by measurements. In order to validate the model, its performance is presented for three different configurations of a real test system, considering both classical sinusoidal excitation and a chirp signal. The paper demonstrates the modularity of the model, which can be adapted to the different configurations by simply adapting the modified component, thus drastically reducing the complexity of modeling a complex ultrasonic system compared to State-of-the-Art models. Based on this work, ultrasonic inspection systems can be optimized for complex applications, such as operation with coded excitation, which is a major challenge for the system components.
]]>Authors: Anna Marzo Bruno Carpani Giuseppe Marghella Concetta Tripepi
Intending to safeguard architectural heritage, the assessment of the health of timber structures is crucial, though challenging, due to the organic nature of wood and to the uncertainties of its preservation state. To this end, useful support is provided by the ICOMOS guidelines, which provide conservation strategies based on thorough diagnosis and safety evaluations. In this context, the study summarized in this paper focuses on the medieval Tower of Montorio, which suffered considerable damage due to the strong earthquake that occurred in those area in September 2003. Its subsequent process of rehabilitation and restoration involved a widespread experimental campaign and the substitution of some timber beams. This circumstance has offered a rare opportunity to study these ancient elements in detail, although they are limited in number. Six beams made of oak and removed from an intermediate floor of the tower were evaluated through a comprehensive approach that included both non-destructive tests (NDT) and destructive tests (DT). Particularly, they were subjected to visual inspections, ultrasonic, sclerometric, and resistographic methods, and destructive four-point bending tests. Overall, the study presented here provides a useful qualitative comparison between them. Results highlighted that relying only on NDT might lead to an overestimation of mechanical properties and that combining NDT with DT is crucial for a more accurate assessment. Therefore, the need to deepen the research on correlations between NDT and DT to obtain reliable values of mechanical properties while respecting the conservation aim was confirmed.
]]>Authors: Moritz Weiss Tobias Meisen
Computed tomography (CT) is a widely utilised imaging technique in both clinical and industrial applications. CT scan results, presented as a volume revealing linear attenuation coefficients, are intricately influenced by scan parameters and the sample’s geometry and material composition. Accurately mapping these coefficients to specific materials is a complex task. Traditionally, material decomposition in CT relied on classical algorithms using handcrafted features based on X-ray physics. However, there is a rising trend towards data-driven approaches, particularly deep learning, which offer promising improvements in accuracy and efficiency. This survey explores the transition from classical to data-driven approaches in material-sensitive CT, examining a comprehensive corpus of literature identified through a detailed and reproducible search using Scopus. Our analysis addresses several key research questions: the origin and generation of training datasets, the models and architectures employed, the extent to which deep learning methods reduce the need for domain-specific expertise, and the hardware requirements for training these models. We explore the implications of these findings on the integration of deep learning into CT practices and the potential reduction in the necessity for extensive domain knowledge. In conclusion, this survey highlights a significant shift towards deep learning in material-resolving CT and discusses the challenges and opportunities this presents. The transition suggests a future where data-driven approaches may dominate, offering enhanced precision and robustness in material-resolving CT while potentially transforming the role of domain experts in the field.
]]>Authors: Bikash Guha Sean Moore Jacques Huyghe
This study addresses the critical issue of cardiovascular diseases (CVDs) as the leading cause of death globally, emphasizing the importance of stent delivery catheter manufacturing. Traditional manufacturing processes, reliant on destructive end-of-batch sampling, present significant financial and quality challenges. This research addresses this challenge by proposing a novel approach: a closed-loop cyber-physical production system (CPPS) employing non-destructive process analytical technology (PAT). Through a mixed-method approach combining a comprehensive literature review and the development of a CPPS prototype, the study demonstrates the potential for real-time quality control, reduced production costs, and increased manufacturing efficiency. Initial findings showcase the system’s effectiveness in streamlining production, enhancing stability, and minimizing defects, translating to substantial financial savings and improved product quality. This work extends the author’s previous research by comparing the validated system’s performance to that of pre-implementation manual workflows and inspections, highlighting tangible and intangible improvements brought by the new system. This paves the way for advanced control strategies to revolutionize medical device manufacturing. Furthermore, the study proposes a generalized CPPS framework applicable across diverse regulated environments, ensuring optimal processing conditions and adherence to stringent regulatory standards. The research concludes with the successful demonstration of innovative approaches and technologies, leading to improved product quality, patient safety, and operational efficiency in the medical device industry.
]]>Authors: Francisca Salgueiro Mário Ribeiro André Carvalho Guilherme Covas Øystein Baltzersen Carla Sofia Proença
The research related to subsea inspection, and the prediction of corrosion is a challenging task, and the progress in this area is continuously generating exciting new developments that may be used in subsea inspection. Wall thickness monitoring is an important tool to control and predict corrosion, such as on platforms for the infrastructure of floating offshore wind power production. This study shows the results obtained in marine environments. For this experiment, a steel plate equipped with ultrasound transducers was placed in seawater to corrode naturally. The sensor test setup consisted of 15 ultrasound transducers and 1 temperature sensor, which were installed in the cassette. The data acquisition system was based on a standard industrial computer with software written in Python and MATLAB. The ultrasound signals were collected at regular intervals and processed to calculate the instantaneous wall thickness. The progress of corrosion was evaluated by trend plots of wall thickness versus time, and the change in shape of the ultrasonic back wall reflection waveform measured by each sensor.
]]>Authors: Luca Porcelli Sultan Dabagov Giovanni Delle Monache Dariush Hampai Giuseppina Modestino Sandra Savaglio
Starting from January 2023, Permanent Staff Personnel and Associated Personnel of INFN-LNF (Istituto Nazionale di Fisica Nucleare—Laboratori Nazionali di Frascati) have founded, and are setting up, the local Astrophysics and Cosmology Team (ACT). The INFN-LNF ACT joined the initial development phases of one of the forthcoming (early 2030) next-generation cosmology space-borne probes, with particular emphasis on (1) thermal balance tests (and correlation to models) of the electronics of interest; (2) (non)destructive irradiation tests of the electronics of interest and X-ray circuitry diagnostics on a specifically dedicated and instrumented optical bench; and (3) joining the simulation-related, and data analysis-related, activities, at both the cosmological and instrumental levels. The INFN-LNF ACT has constituted an Integrated Test Facility (ITF), which is being instrumented in a dedicated space and will also make use of the pre-existing INFN-LNF infrastructures. In the following, as a first contribution, mainly related to what was completed in late 2023 and early 2024, the activities of the commissioning and setup of the so-called ‘pocket’ cryostat are described, linking them to the envisaged thermal balance tests (and correlation to the models) of the electronics of interest. While mainly devoted to cosmology-oriented tasks, the INFN-LNF ACT ‘pocket’ cryostat will, in principle, be available to the wider community for other dedicated activities.
]]>Authors: Alfredo Lamberti Wouter Van Eesbeeck Steve Nardone
Additive Manufacturing (AM) of parts used in nuclear power plants can solve many issues like those related to obsolescence. Of the gap limiting the use of AM parts in nuclear is the need of reliable non-destructive inspection capable to meet the qualification requirements. Recently, efforts in this direction have been made worldwide within several research projects, like the EU Horizon 2020 NUCOBAM. In the framework of NUCOBAM, this article presents the activity related to the inspection of 316-L AM nuclear parts produced by L-PBF and inspected via advanced ultrasonic (UT) methods, like MultiPoint Focusing (MPF) and Total Focusing Method (TFM). Multiple UT array probes are used, linear, matrix and annular. Emphasis is dedicated to the inspection of classified valve bodies produced with known internal seeding flaws. The analysis of the results shows the effect of AM induced anisotropy on the propagation of the ultrasonic wave characteristics, the sound velocity increased with 3% when the sound beam deviated 15° against the perpendicular axis. The TFM method contributed significantly regarding defect detection, Signal to Noise Ratios (SNR) increased with at least 9 dB compared to the Multi-Point Focusing method. Smaller errors were noticed when examination frequency was increased and TFM was applied. The combination of an annular array with TFM and mechanical scanning demonstrated to be the best approach.
]]>Authors: Ahmad Zaki Nabilah Cantika Aprilia Sri Atmaja P. Rosyidi Khairil Mahbubi
The incorporation of natural fibers into concrete has recently emerged as a popular approach in the field of construction materials due to its sustainability and environmental friendliness. In comparison to artificial fibers, natural fibers are more cost-effective and widely available globally. Among the various natural fibers, coconut fiber (CF) stands out for its unique set of advantages. This study aims to investigate the mechanical properties and durability of coconut-fiber-reinforced self-healing concrete (CFR-SHC) in the context of corrosion resistance. Additionally, Bacillus subtilis bacteria (10% by mass) was incorporated into the CFR-SHC. The impact of ±50 mm long CF with varying contents of 0.25%, 0.5%, and 0.75% by mass was examined. Specimens were subjected to corrosion acceleration for 48, 96, and 168 h. Non-destructive testing (NDT) methods of Electrical Resistivity (ER) and Impact Echo (IE) were conducted to test the corrosion resistance. The experimental results demonstrate that CFR-SHC increased the compressive strength by 6% and the flexural strength by 40%. CFR-SHC also exhibits excellent resistance to corrosion, characterized by low inrush current, high ER value, and high IE frequency. The most favorable overall outcomes were observed for the CFR-SHC sample containing 0.5% of the cement mass.
]]>Authors: Dora Francesca Barbolla Ilaria Miccoli Immacolata Ditaranto Giuseppe Scardozzi Francesco Giuri Ivan Ferrari Giovanni Leucci
Geophysics is a fundamental tool to detect buried structures of archaeological interest through non-destructive techniques. The Messapian city walls in Ugento (Puglia, southern Italy) are of great archaeological importance, and some sections are still visible. In order to locate a stretch of the city walls, geophysical prospections were performed using the low-frequency electromagnetic method and ground-penetrating radar. The surveys were carried out in a peripheral area of Ugento, near a visible section of the city walls. The analysis and interpretation of the measured data revealed clear anomalies that could be ascribed to the city walls that aligned with an adjacent section of the visible walls. Archaeological excavation campaigns found a part of the walls and some important elements, as identified by the geophysical data interpretation.
]]>Authors: Fabricio Leiva-Villacorta Adriana Vargas-Nordcbeck
The work conducted in this study was designed to establish achievable testing tolerances for non-destructive pavement density measurements using Density Profiling Systems (DPSs). Nine and six sensors were used to determine the precision of repeatability and reproducibility in the laboratory and the field, respectively. A minimum of six sensors (considered in this study as independent laboratories) were needed to comply with the minimum number of participants required in the current ASTM standard practice (ASTM E691). The methodology included the development of laboratory precision evaluation with a total of nine sensors and two different mixtures (9.5 mm fine-graded mix, 19.0 mm coarse-graded mix) compacted at four density levels (97%, 94%, 91%, and 88% of Gmm). For the field portion of this study, pavement sections built at the National Center for Asphalt Technology (NCAT) Test Track in 2021 served as experimental variables. These sections were built with fine-graded asphalt mixtures and open-graded mixes as wearing courses. Additionally, the pavement sections included three underlying materials: new asphalt (binder layer), milled asphalt surface, and granular base, with thicknesses ranging from 3.8 to 13.9 cm. Density profile testing was conducted at two locations: within the mat (center of the lane) and along the joint. Computed precision statements regarding dielectric values within and between laboratories were about double for field results compared to laboratory results. However, when converted to density, the statements were significantly below the reported statements for Bulk Specific Gravity and Vacuum Sealing in the laboratory and Nuclear and Electromagnetic density gauges in the field.
]]>Authors: Amr El-Nemr Ibrahim G. Shaaban
Concrete is the second most common material demanded over the world. Recently, a trending issue is the vast tracking in constructing infrastructure to ensure traffic movement and life quality. Concrete types such as self and rolled compacted concrete offer magical solutions ensuring vast infrastructure and life quality. However, these structures must be assessed using non-destructive testing methods to observe the difference between the concrete types. Several studies have used recycled waste, specifically the crumb rubber extracted from old tires, as a potential replacement for natural aggregate in concrete manufacturing. However, limited research has been devoted to nondestructive testing of produced concrete to further evaluate existing concrete elements containing crumb rubber. This study investigates the self and rolled compacted concrete in comparison with normal ones, in addition to using chopped rubber as recycled materials. This study examines the concrete manufactured destructively by evaluating its compressive, tensile, and flexural strength, in addition to impact resistance, and correlates those results with the non-destructive such as Schmit hammer and Ultrasonic Pulse (UPV) for extended utilization of the concrete produced and data publication. The results showed unique performance and a high potential for data contribution to the extensive utilization of self-compacted rubberized concrete and rolled compacted concrete.
]]>Authors: Mireia Conde Maria Mercè Solé Sílvia Sorolla Concepció Casas Anna Bacardit
This study explored the potential application of polyphenols from Pinus halepensis bark in leather tanning. The primary objective was to characterize these polyphenols. The extraction and atomization processes proved efficient, reducing moisture content to 7.4%, increasing tannin content from 26.2% to 45.1%, and reducing insoluble substances by 77.5%. High-performance liquid chromatography (HPLC) coupled with mass spectrometry identified and quantified various polyphenolic compounds, including (+)-catechin, (+)-taxifolin, protocatechuic acid, and procyanidin B2. Notably, tannic catechin dimers were detected. Lignin was effectively removed through filtration. Concentrations of protocatechuic acid, (+)-catechin, (+)-taxifolin, (−)-epicatechin, and procyanidin B1 were significantly higher in the extract than in the powder, with the extract showing 1214.3 mg/kg of protocatechuic acid, 2098.0 mg/kg of (+)-catechin, 4017.0 mg/kg of (+)-taxifolin, 2163.0 mg/kg of (−)-epicatechin, and 917.0 mg/kg of procyanidin B1.
]]>Authors: Sara Shahsavarani Clemente Ibarra-Castanedo Fernando Lopez Xavier P. V. Maldague
Infrared and visible imaging play crucial roles in non-destructive testing, where accurate defect segmentation and detection are paramount. However, the scarcity of annotated training data or the limited number of data availability often poses a challenge. To address this, we propose an innovative framework tailored to the domain of infrared and visible imaging, integrating segmentation and detection tasks. The proposed approach eliminates the dependency on annotated defect data during training, enabling models to adapt to real-world scenarios with limited annotations. By utilizing super-pixel segmentation and texture analysis, the proposed method enhances the accuracy of defect detection. Concrete structures, globally subjected to aging and degradation, demand constant monitoring for structural health. Traditional manual crack detection methods are labor-intensive, necessitating automated systems. The proposed approach combines deep learning-based super-pixel segmentation with texture analysis, offering a solution for limited-defect-data situations. Utilizing convolutional neural networks (CNNs) for super-pixel segmentation and texture features for defect analysis, the proposed methodology improves the efficiency and accuracy of crack detection, especially in scenarios with limited labeled data or a limited number of data available. Evaluation on public benchmark datasets have validated the effectiveness of the proposed approach in detecting cracks in concrete structures.
]]>Authors: Amir-M. Naddaf-Sh Vinay S. Baburao Hassan Zargarzadeh
Automated ultrasonic testing (AUT) is a nondestructive testing (NDT) method widely employed in industries that hold substantial economic importance. To ensure accurate inspections of exclusive AUT data, expert operators invest considerable effort and time. While artificial intelligence (AI)-assisted tools, utilizing deep learning models trained on extensive in-laboratory B-scan images, whether they are augmented or synthetically generated, have demonstrated promising performance for automated ultrasonic interpretation, ongoing efforts are needed to enhance their accuracy and applicability. This is possible through the evaluation of their performance with experimental ultrasonic data. In this study, we introduced a real-world ultrasonic B-scan image dataset generated from proprietary recorded AUT data during industrial automated girth weld inspection in oil and gas pipelines. The goal of inspection in our dataset was detecting a common type of defect called lack of fusion (LOF). We experimentally evaluated deep learning models for automatic weld defect detection using this dataset. Our assessment covers the baseline performance of state-of-the-art (SOTA) models, including transformer-based models (DETR and Deformable DETR) and YOLOv8. Their flaw detection performance in ultrasonic B-scan images has not been reported before. The results show that, without heavy augmentations or architecture customization, YOLOv8 outperforms the other models with an F1 score of 0.814 on our test set.
]]>Authors: Cyrus Bourbour
Ultra-high-performance concrete (UHPC) is widely used because of its exceptional properties, such as high compressive and flexural strength, low permeability, and resistance to abrasion and chemical attack. It is commonly employed for intricate constructions like skyscrapers, precast concrete components, and infrastructure. Nevertheless, the incorporation of appropriate fibers into UHPC is carried out in order to accomplish objectives such as augmenting strength, enhancing toughness, and regulating cracking. This study employed magnetite as an additive to a UHPC block in order to examine the mechanical characteristics of a newly cast UHPC block. Acoustic emission was employed to evaluate the damage to the UHPC block for tracking purposes. Acoustic emission is a non-invasive testing technique that does not cause harm to the specimen when it is exposed to a load. On the basis of this, many critical locations that indicated the propagation of cracks were analyzed, as well as various loading stages across the specimen. The b-value is a method that can evaluate the extent of damage by analyzing the amplitude distribution. Distinct paths of b-values were noted for each loading stage, indicating major damage scenarios based on their slopes.
]]>Authors: Roland Linck Andreas Stele Christoph Schimmer
For several years, photogrammetric surveys are quite often applied for surveying cultural heritage. For a detailed and accurate result, it is indispensable that the calculated point cloud represents the original geometry of the building in high quality, with vertical and horizontal structures in the same manner. Whereas many published papers in the field of digital heritage documentation are dealing with creating such models or using photogrammetric data for damage detection, only a few papers are investigating the crucial fact of accuracy and how it can be influenced by the used camera resolution. We want to present a case study, in which we compare the photogrammetric point cloud of a normal consumer drone with a high-end device. Beside the actual survey results showing the details of a Romanesque church, a statistical calculation of deviation is shown. It becomes evident that there is only a difference within some millimetres between the corresponding point clouds in most parts of the monument. Hence, it can be stated that, for most applications in heritage protection, affordable consumer unmanned aerial vehicles (UAVs) are sufficient. Only if faint details have to be resolved will a better resolution of the mounted camera be required.
]]>Authors: Paola Boldrin Giacomo Fornasari Enzo Rizzo
Infrastructure bridges play a crucial role in fostering economic and social development. However, the adverse effects of natural hazard and weather degradation, coupled with escalating rates of traffic, pose a significant threat. The resultant strain on the structure can lead to undue stress, elevating the risk of a critical asset failure. Hence, non-destructive testing (NDT) has become indispensable in the surveillance of bridge infrastructure. Its primary objectives include ensuring safety, optimizing structural integrity, minimizing repair costs, and extending the lifespan of bridges. NDT techniques can be applied to both existing and newly constructed bridge structures. However, it is crucial to recognize that each NDT method comes with its own set of advantages and limitations tailored to specific tasks. No single method can provide an effective and unequivocal diagnosis on its own. Among the various NDT methods, Ground Penetrating Radar (GPR) has emerged as one of the most widely employed techniques for monitoring bridges. In fact, recent technical regulations now mandate the use of GPR for bridge monitoring and characterization, underscoring its significance in ensuring the structural health and longevity of these critical infrastructures. Ground Penetrating Radar (GPR) stands out as one of the most highly recommended non-destructive methods, offering an efficient and timely assessment of the structural conditions of infrastructure. Recognizing the pivotal role of non-destructive testing (NDT) in this context, this paper aims to elucidate recent scientific endeavors related to the application of GPR in bridge engineering structures. The exploration will commence with a focus on studies conducted both at the model level within laboratory settings and on real cases. Subsequently, the discussion will extend to encompass the characterization and monitoring of the bridge’s main elements: slab, beam, and pillar. By delving into these scientific experiences, this paper intends to provide valuable insights into the efficacy and applicability of GPR in assessing and ensuring the structural integrity of bridges. This paper provides a concise survey of the existing literature on the application of Ground Penetrating Radar (GPR) in the assessment of bridges and viaducts constructed with masonry and reinforced concrete, taking into account papers of journal articles and proceedings available on open databases. Various approaches employed in both laboratory and field settings will be explored and juxtaposed. Additionally, this paper delves into discussions on novel processing and visualization approaches, shedding light on advancements in techniques for interpreting GPR data in the context of bridge and viaduct evaluations.
]]>Authors: Thiago Luiz Lara Oliveira Daniel Brighenti Bortoluzzi Lorena Cristina Miranda Barbosa Antônio Carlos Ancelotti
The use of composites in industry is increasing due to their ability to replace traditional materials. Carbon fiber-reinforced polymers offer a favorable strength-to-weight ratio, making them advantageous in numerous applications. Delamination is a common failure mode for composite materials, making it a crucial factor in ensuring material safety during service life. While fiber orientation in composites is designed for specific directional reinforcement, out-of-plane loads are often neglected, posing a critical challenge. Implementing through-thickness reinforcement, such as tufting, can enhance out-of-plane resistance, enabling more accurate structural designs. Non-destructive testing methods, particularly acoustic emission, play a significant role in ensuring component safety by detecting early damage and flaws. This study focused on monitoring mode II interlaminar fracture toughness and end-notched flexure (ENF), using acoustic emissions to compare the performance of samples with different through-thickness reinforcements against that of nonreinforced samples. The research analyzed acoustic emission patterns during testing, revealing a strong correlation with failure stages and the resistance induced by reinforcements. This approach provided valuable insights into damage characterization, supported by fractography analysis, especially concerning the final stages of failure due to damage, and the effects of different thread reinforcements. Acoustic emission proved crucial for real-time monitoring, enabling informed decisions to be made regarding component repair and lifespan extension in composite materials.
]]>Authors: Angeliki Armeni Andreas Loizos
Airfield pavements are important assets that have to secure the safe operation of an airport. On this basis, assessing and reporting the bearing capacity of an airfield runway pavement is a critical task. Recently, the Aircraft Classification Rating-Pavement Classification Rating (ACR-PCR) system has been introduced, which uses the PCR index for expressing the bearing capacity of an airfield pavement. In order to accurately determine PCR, the mechanical characteristics and the thicknesses of the individual layers of a pavement are required. For this purpose, it is not seldom that in the absence of resources dedicated to detailed pavement evaluation procedures, assumptions for the material characteristics of the pavement considering typical materials may be made, while pavement thicknesses may be derived by pavement design records. The present paper highlights the importance of using Non-Destructive Testing (NDT) for accurately assessing the in-situ condition of a flexible runway pavement and determining the PCR index. In order to achieve the goal of the investigation, measurements were performed along the flexible pavement of an airport runway. In addition, the paper focuses on the impact of the variation of the thickness and of the mechanical characteristics of the asphalt concrete layers on the PCR index and on the interpretation of the results considering the acceptance of aircraft operations by airport authorities.
]]>Authors: Moritz Weiss Nick Brierley Mirko von Schmid Tobias Meisen
Material-resolving computed tomography is a powerful and well-proven tool for various clinical applications. For industrial scan setups and materials, several problems, such as K-edge absence and beam hardening, prevent the direct transfer of these methods. This work applies dual-energy computed tomography methods for material decomposition to simulated phantoms composed of industry-relevant materials such as magnesium, aluminium and iron, as well as some commonly used alloys like Al–Si and Ti64. Challenges and limitations for multi-material decomposition are discussed in the context of X-ray absorption physics, which provides spectral information that can be ambiguous. A deep learning model, derived from a clinical use case and based on the popular U-Net, was utilised in this study. For various reasons outlined below, the training dataset was simulated, whereby phantom shapes and material properties were sampled arbitrarily. The detector signal is computed by a forward projector followed by Beer–Lambert law integration. Our trained model could predict two-material systems with different elements, achieving a relative error of approximately 1% through simulated data. For the discrimination of the element titanium and its alloy Ti64, which were also simulated, the relative error increased to 5% due to their similar X-ray absorption coefficients. To access authentic CT data, the model underwent testing using a 10c euro coin composed of an alloy known as Nordic gold. The model detected copper as the main constituent correctly, but the relative fraction, which should be 89%, was predicted to be ≈70%.
]]>Authors: Eduardo Moreno Roberto Giacchetta Ricardo Gonzalez David Sanchez Olalla Sanchez-Sobrado Andrea Torre-Poza Guillermo Cosarinsky Wagner Coelho
This paper describes the design and implementation of an ultrasonic non-contact air-coupled technique (UNCACT) using antisymmetric Lamb waves (ALW) for NDT assessments in novel composite sandwich plates of a car body shell. This technique is complemented with a C-Scan image implementation using guided waves. The finite element method (FEM) was developed using Comsol 6.1 for the interpretation of the several wave modes presented in the experiments, including the ALW mode. This FEM model is indispensable for the correct interpretation of the received signals and contributes to a better implementation of this technology. This is a novel contribution building upon previously reported work. Additionally, the phase velocity method (PVM) was applied for the verification of the ALW mode in the portion of the RF signal necessary for the C-Scan image.
]]>Authors: Chang Liu Chen An Yifan Yang
In order to solve the problems of low efficiency, time consumption and high costs in the detection of defects on wind turbine surfaces in industrial scenarios, an improved YOLOv5 algorithm for wind turbine surface defect detection is proposed, named YOLOv5s-L. Firstly, the C3 module of YOLOv5s is replaced with the C2f module, which is more abundant in gradient flow, to enhance the ability of feature extraction and feature fusion. Secondly, the Squeeze and Excitation (SE) module is embedded in the YOLOv5 Backbone network to filter out redundant feature information and retain important feature information. Thirdly, the weighted Bidirectional Feature Pyramid Network (BiFPN) is introduced to replace the FPN + PAN, which can achieve a higher level of feature fusion while keeping the weight light. Finally, the Focal Loss function is used to replace the CIOU Loss function of the YOLOv5 algorithm to optimize the training model and improve the accuracy of the algorithm. The experimental results show that, compared with the traditional YOLOv5 algorithm, the average precision mAP is improved by 1.9%, and the frame rate FPS can reach 145 F/s without increasing the model parameters; it can satisfy the requirements for real-time, accurate detection on mobile devices. This method provides effective support for surface defect detection of wind turbines and provides reference for intelligent wind farm operation and maintenance.
]]>Authors: Ruilin Lyu Tian Meng Yuchun Shao Jorge Ricardo Salas Avila Wuliang Yin
Coercivity is the strength of the reverse magnetic field required to demagnetize a material after saturation, and it is an indication of the hardness of magnetic materials. Air gaps cause errors in coercivity measurement referred to as the lift-off effect. This paper proposes a new method to address this issue by incorporating additional inductance measurements and formulating a calibration method. The calibration principle is based on the fact that both the coercivity and the inductance measurements change with the variation of air gaps. This paper starts by finding how coercivity changes with air gaps between the sensor and the sample, then derives the coefficients for the coercivity–inductance relationship for different samples. A correction method is then proposed to predict the base coercivity (i.e., the coercivity when the air gap = 0) using the inductance and coercivity measurement results at an unknown lift-off. The measurement system was implemented, and experimental results suggest the error caused by air gaps can be reduced from 40% to less than 10%.
]]>Authors: Ajay D. Pingale Diplesh Gautam Ayush Owhal Dhruv Deshwal Sachin U. Belgamwar Venkatesh K. P. Rao
In the past several decades, many destructive and non-destructive testing techniques have been developed to evaluate the characteristics of metal matrix composites (MMCs). This research aims to calculate the mechanical properties of the Al@Al2O3 composites by varying alumina nanoparticles (Al2O3 NPs) content using a non-invasive, position sensing detector (PSD) unit-based optical method. The composite was prepared by a powder metallurgy technique, and its characterization was conducted using SEM and XRD to understand its surface morphology and microstructure. The natural frequency and Young’s modulus of the composite were estimated experimentally. Young’s modulus was calculated using this natural frequency. The proposed study shows that Young’s modulus of the composite increases with an increase in Al2O3 NPs content in the composition, irrespective of the testing method. Along with this, natural frequency also increases with the increase in the Al2O3 NPs content. Evaluated properties were compared with the numerical modeling using COMSOL Multiphysics. The experimental and numerical results are equivalent and within the margin of error. This study illustrates the development of an experimental approach for evaluating the mechanical properties of a composite material. This experimental approach can be used whenever sample dimension and space are constrained to evaluate the mechanical behavior of nanomaterials and nanocomposites.
]]>Authors: Mohammad M. Bazrafkan Marcus Rutner
This paper reviews the state-of-the-art approaches in defect localization and specifies the remaining questions and challenges. Furthermore, this study presents a novel defect localization methodology using the nonlinear interaction of primary Lamb wave modes and vibroacoustic modulation (VAM), combined with damage imaging, to address the current shortcomings of defect localization. The study investigates this methodology experimentally with respect to defect interpretation, resolution, and applicability. Two Lamb waves with high and low frequencies, one being continuous and the other a tone burst, were excited using two different piezoelectric sensors. The amplitude of the measured signal at the first sideband frequency was evaluated with a short-time Fourier transform (STFT) and used for damage imaging via the delay and sum method. This study also includes a discussion on identifying the source of nonlinearity reflected in the first sideband. The experimental measurements prove that the localization of defect nonlinearity is possible with high accuracy, without the need for a baseline measurement, and with a minimum number of sensors. Sensitivity measurements with respect to the required length of the high-frequency tone burst and the sensor arrangement were also conducted.
]]>Authors: Fabio Tosti
It is with great pleasure that the journal NDT (ISSN 2813-477X) [...]
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