NDT — Journal of Non-Destructive Testing

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 $A_0$, $S_0$, and $S{H}_1$. 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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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 $Ti{O}_2$ nanoparticle and a hybrid $Ti{O}_2/Au$ 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 $Ti{O}_2/Au$ 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 $Ti{O}_2/Au$ 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. Full article

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. Full article

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. Full article

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

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

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. Full article

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

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. Full article

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. Full article