NDT, Volume 3, Issue 3 (September 2025) – 6 articles

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