Search

In recent years, with the development of materials science, the joining technology is also constantly upgraded, ultrasonic testing technology can more accurately detect the defects or failures of new materials in the new joining process, which has extensive applications in the field of engineering joining, such as welding, mechanical joining, and adhesive bonding. This paper introduces the common non-destructive testing techniques, including magnetic particle testing, eddy current testing, penetration testing, radiographic testing, and ultrasonic testing. The main principles and devices of ultrasonic testing are introduced, including air-coupled ultrasonic testing, electromagnetic ultrasonic testing, laser ultrasonic testing, and so on. The current status of the application of ultrasonic testing in welded joints, riveted joints, bonded joints, and bolted joints is summarized and reviewed. Finally, the drawbacks of ultrasonic testing in this field are discussed, as well as prospective research directions. [ABSTRACT FROM AUTHOR]

The paper is devoted to a problem of acquiring elastic properties of a composite material from the vibration testing data with a simplified experimental acquisition scheme. The specimen is considered to abide by the linear elasticity laws and subject to viscoelastic damping. The boundary value problem for transverse movement of such a specimen in the frequency domain is formulated and solved with finite-element method. The correction method is suggested for the finite element matrices to account for the mass of the accelerometer. The problem of acquiring the elastic parameters is then formulated as a nonlinear least-square optimization problem. The usage of the automatic differentiation technique for stable and efficient computation of the gradient and hessian allows to use well-studied first and second order local optimization methods. We also explore the possibility of generating initial guesses for local minimization by heuristic global methods. The results of the numerical experiments on simulated data are analyzed in order to provide insights for the following real life experiments. [ABSTRACT FROM AUTHOR]

Carbon Fiber Reinforced Polymer (CFRP), a novel high-performance material, has found extensive use across various domains. Due to the intricate structure of CFRP and variability in its manufacturing process, such as differences in resin curing speeds or carbon fiber alignment, defects including voids and inclusions can inevitably occur. These defects can compromise the material's performance and reliability, making Non-Destructive Testing (NDT) essential for early defect detection and real-time monitoring. This paper reviews existing NDT technologies like visual inspection, computed tomography (CT), ultrasonic testing, acoustic emission testing, infrared thermal imaging, eddy current testing, and Raman spectroscopy. To facilitate performance comparison, we use spider charts to illustrate key aspects of each technique, including detection accuracy, damage localisation, interface details, and online monitoring capabilities. These charts specifically highlight the superior overall performance of CT. The principles, applications, and cutting-edge advancements in NDT techniques and Structural Health Monitoring systems (SHM) are systematically discussed. This review delves into the principles, strengths, and limitations of CFRP damage detection methods, includes detailed examples and provides a selection criterion for the work of NDT researchers. [ABSTRACT FROM AUTHOR]

The paper deals with the non-destructive experimental testing of the reinforced concrete beams under progressive corrosion. A series of experiments using electrical potential, ultrasound and low-frequency vibrations techniques are reported. Electrical potential and natural frequencies were used to characterise and monitor the corrosion process at its initial state. The P-wave velocity measurements were proved to be effective in quantitative assessment of the level of corrosion as it progresses. The possibility of early detection of damage using a proposed damage index and diagnostic framework is promising for possible applications in the non-invasive diagnostics of reinforced concrete elements. [ABSTRACT FROM AUTHOR]

The new brake disc was evaluated for microstructure and hardness by the conventional destructive tests and non-destructive Barkhausen noise method (BNM). Ten non-destructive measurements were carried out in different areas of a brake disc, which were then cut out and made into metallographic test samples. Qualitative and quantitative analysis of graphite precipitates was performed to assess their volume in material matrix, anisotropy and size. Subsequently, graphs showing the relationships between selected stereological parameters of graphite precipitates and parameters determined from the RMS envelope of Barkhausen noise were elucidated. Similar relationships between hardness and parameters coming from non-destructive tests were carried out. Magnetic parameters that specified the size of a graphite precipitate was selected. In addition, repeatability studies using BNM were carried out in the areas of the material with the smallest and largest average size of graphite precipitates. A linear relationship between amplitude of BN and length of graphite flakes was found. The paper presents the possibilities of assessing the volume and size of graphite precipitates, as well as cast iron hardness using BNM. [ABSTRACT FROM AUTHOR]

This paper presents the current state of conservation of historic roof churches located in the Żuławy of Gdańsk (Poland). It also describes the architecture of these temples, the region itself and old carpentry techniques for constructing roof trusses. Interdisciplinary tests were carried out in six churches. The geometry of the load bearing structures, the moisture content and the carpentry technique were specified. The field survey also included visual inspections and non-destructive testing of timber structural elements of the roof constructions. The ground penetrating radar and ultrasonic testing methods were used to assess the structure and extent of the damage to the timber elements. The interdisciplinary research presented in this article is important in the planning of historic buildings conservation works and it might be applied to other timber structures. [ABSTRACT FROM AUTHOR]

The identification of residual stresses (RS) in components made by selective laser melting (SLM) is necessary for subsequent technological optimization. The presented research is devoted to evaluating the influence of the combination of laser power (P), scanning velocity (v) and the rarely considered number of layers (nL) on surface residual stresses in SLM stainless steel SS 316L. Experimental parameters were set based on the Design of Experiment (DoE) method, with follow-up X-ray diffraction (XRD) measurements and data processing using analysis of variance (ANOVA) and regression analysis. The obtained data are a valuable stepping-stone for the subsequent design of research focused on the application of sustainable eco-friendly Abrasive Water Jet (AWJ) peening for RS modification in the evaluated material. [ABSTRACT FROM AUTHOR]

In response to the growing global demand for both energy and a clean environment, there has been an unprecedented rise in the utilization of renewable energy. Wind energy plays a crucial role in striving for carbon neutrality due to its eco-friendly characteristics. Despite its significance, wind energy infrastructure is susceptible to damage from various factors including wind or sea waves, rapidly changing environmental conditions, delamination, crack formation, and structural deterioration over time. This research focuses on investigating non-destructive testing (NDT) of wind turbine blades (WTBs) using approaches based on the vibration of the structures. To this end, WTBs are first made from glass fiber-reinforcement polymer (GFRP) using composite molding techniques, and then a short pulse is generated in the structure by a piezoelectric actuator made from lead zirconate titanate (PZT-5H) to generate guided waves. A numerical approach is presented based on solving the elastic time-harmonic wave equations, and a laser Doppler vibrometer (LDV) is utilized to collect the vibrational data in a remote manner, thereby facilitating the crack detection of WTBs. Subsequently, the wave propagation characteristics of intact and damaged structures are analyzed using the Hilbert–Huang transformation (HHT) and fast Fourier transformation (FFT). The results reveal noteworthy distinctions in damaged structures, where the frequency domain exhibits additional components beyond those identified by FFT, and the time domain displays irregularities in proximity to the crack region, as detected by HHT. The results suggest a feasible approach to detecting potential cracks of WTBs in a non-contact and reliable way. [ABSTRACT FROM AUTHOR]

Coplanar array capacitive imaging represents a non-destructive testing technology. To address the challenge of reduced utilisation of the high-sensitivity region caused by the ‘soft field’ effect characterised by severe nonlinear and inhomogeneous distribution of coplanar array capacitance sensitivity, resulting in difficulties in solving the problem of medium distribution, the proposed approach involves coplanar array capacitance sensor rotational scanning technology. This paper establishes both a physical rotation model and a sensitive field rotation model for a coplanar array capacitance sensor, demonstrating the inability to resolve the low-sensitivity region for medium distribution and illustrating how rotary scanning detection can enhance the utilisation rate of the high-sensitivity region. In conjunction with improved regional energy contrast, the images acquired under various rotation angles are fused, and experimental verification is conducted. The experimental results underscore that rotational scanning detection techniques can increase the utilisation rate of the high-sensitivity region within the coplanar array capacitance sensor by 74.69%. This enhancement effectively captures essential information within the reconstructed image and successfully addresses the challenge of solving difficulties in dielectric distribution. The research outcomes provide valuable insights for advancing non-destructive testing technologies employing coplanar array capacitance sensors. [ABSTRACT FROM AUTHOR]

The maturity of fruits and vegetables such as tomatoes significantly impacts indicators of their quality, such as taste, nutritional value, and shelf life, making maturity determination vital in agricultural production and the food processing industry. Tomatoes mature from the inside out, leading to an uneven ripening process inside and outside, and these situations make it very challenging to judge their maturity with the help of a single modality. In this paper, we propose a deep learning-assisted multimodal data fusion technique combining color imaging, spectroscopy, and haptic sensing for the maturity assessment of tomatoes. The method uses feature fusion to integrate feature information from images, near-infrared spectra, and haptic modalities into a unified feature set and then classifies the maturity of tomatoes through deep learning. Each modality independently extracts features, capturing the tomatoes' exterior color from color images, internal and surface spectral features linked to chemical compositions in the visible and near-infrared spectra (350 nm to 1100 nm), and physical firmness using haptic sensing. By combining preprocessed and extracted features from multiple modalities, data fusion creates a comprehensive representation of information from all three modalities using an eigenvector in an eigenspace suitable for tomato maturity assessment. Then, a fully connected neural network is constructed to process these fused data. This neural network model achieves 99.4% accuracy in tomato maturity classification, surpassing single-modal methods (color imaging: 94.2%; spectroscopy: 87.8%; haptics: 87.2%). For internal and external maturity unevenness, the classification accuracy reaches 94.4%, demonstrating effective results. A comparative analysis of performance between multimodal fusion and single-modal methods validates the stability and applicability of the multimodal fusion technique. These findings demonstrate the key benefits of multimodal fusion in terms of improving the accuracy of tomato ripening classification and provide a strong theoretical and practical basis for applying multimodal fusion technology to classify the quality and maturity of other fruits and vegetables. Utilizing deep learning (a fully connected neural network) for processing multimodal data provides a new and efficient non-destructive approach for the massive classification of agricultural and food products. [ABSTRACT FROM AUTHOR]

The prerequisite of the weld bead finishing is intricately linked to the quality of the welded joint. It constitutes the final, yet pivotal, stage in its formation, significantly influencing the reliability of structural components and machines. This article delineates an innovative post-weld surface finishing method, distinguished by the movement of a specialized cutting tool along a butt weld. This method stands out due to its singular approach to machining allowance, wherein the weld bead height is considered and eradicated in a single pass of the cutting tool. Test samples were made of AISI 304L, AISI 316L stainless steels and EN AW-5058 H321, EN AW-7075 T651 aluminum alloys butt-welded with TIG methods. Following the welding process, the weld bead was finished in accordance with the innovative method to flush the bead and the base metal's surface. For the quality control of welded joints before and after the weld finishing, two non-destructive testing methods were chosen: Penetrant Testing (PT) and Radiographic Testing (RT). This article provides results from the examination of 2D profile parameters and 3D stereometric characteristics of surface roughness using the optical method. Additionally, metallographic results are presented to assess changes in the microstructure, the microhardness, and the degree of hardening within the surface layer induced by the application of the innovative post-weld finishing method. [ABSTRACT FROM AUTHOR]

This paper deals with determining degradation changes in alkaline-activated slag-based building materials. It describes methods for determining dynamic elastic moduli obtained by acoustic non-destructive methods, including the ultrasonic pulse method and the resonance method. A finegrained mortar was chosen as the initial mix for the implementation of the experiment, the binder part of which was sodium hydroxide-activated blast-furnace granulated slag. Beams with dimensions of 40 × 40 × 160mm were chosen as test bodies, for which acoustic non-destructive methods monitored changes in the material structure during degradation processes. [ABSTRACT FROM AUTHOR]

Ensuring the security of germplasm resources is of great significance for the sustainable development of agriculture and ecological balance. By combining the morphological characteristics of maize seeds with hyperspectral data, maize variety classification has been achieved using machine learning algorithms. Initially, the morphological data of seeds are obtained from images, followed by the selection of feature subsets using Recursive Feature Elimination (RFE) and Select From Model (SFM) methods, indicating that features selected by RFE exhibit better performance in maize seed classification. For hyperspectral data (350–2500 nm), Competitive Adaptive Re-weighted Sampling (CARS) and the Successive Projections Algorithm (SPA) are employed to extract feature wavelengths, with the SPA algorithm demonstrating superior performance in maize seed classification tasks. Subsequently, the two sets of data are merged, and a Random Forest (RF) classifier optimized by Grey Wolf Optimization (GWO) is utilized. Given the limitations of GWO, strategies such as logistic chaotic mapping for population initialization, random perturbation, and final replacement mechanisms are incorporated to enhance the algorithm's search capabilities. The experimental results show that the proposed ZGWO-RF model achieves an accuracy of 95.9%, precision of 96.2%, and recall of 96.1% on the test set, outperforming the unimproved model. The constructed model exhibits improved identification effects on multi-source data, providing a new tool for non-destructive testing and the accurate classification of seeds in the future. [ABSTRACT FROM AUTHOR]

Non-destructive thermographic testing of damaged composite laminates modeled from the homogenization of fiber-reinforced polymers is a challenge, both because of its underlying complexity and because of the difficulties encountered in the quantification of uncertainties related to the identification and characterization of defects. To provide a rigorous framework that accepts data from different modalities and allows data fusion as well, a Bayesian neural network (BNN) [I. Kononenko, Biological Cybernetics61(5) (1989), 361–370] with two input streams is proposed, with a focus on local inter-layer delaminations identification and characterization. [ABSTRACT FROM AUTHOR]

One of the effective methods of non-destructive testing of structures is active vibration diagnostics. This approach consists of the local dynamic impact of the actuator on the structure and the registration of the vibration response. Testing of massive reinforced concrete structures is carried out with the use of actuators, which are able to create sufficiently high-impact loads. The actuators, which are based on piezoelectric elements, cannot provide a sufficient level of force and the areas where it is possible to register the vibrations excited by such actuators are quite small. In this paper, we propose a variant of a piezoactuator with attached mass, which ensures an increase in the level of dynamic impact on the structure. The effectiveness of this version is verified by numerical modeling of the dynamic interaction of the actuator with a concrete slab. The simulation was carried out within the framework of the theory of elasticity and coupled electroelasticity. An algorithm for selecting the value of the attached mass is described. It is shown that when vibrations are excited in a massive concrete slab, an actuator with an attached mass of 1.3 kg provides a 10,000-fold increase in the force compared to an actuator without attached mass. In the pulse mode, a 100-fold increase in force is achieved. [ABSTRACT FROM AUTHOR]

The acoustic emission (AE) monitoring of failures, fractures and instabilities evolving within a snowpack may provide vital information about the avalanche formation preceding release processes. In this work, AE activity was collected continuously over five years from the snowpack built on avalanche starting zone in Great Himalaya, and the analyses are presented in the context of an avalanche release and two slab fracturing events detected successfully during this study period. For non-invasive detection of AE activity from snow, a specialised sensor-arrestor array was deployed over the avalanche slope. In our investigations, an abnormal AE activity was observed both during the slab fracturing as well as the avalanche occurrence periods. For AE-based direct assessment of snow instability, a method is proposed to estimate instability index (βin), based on AE count and amplitude values of a hit, and it is applied for analyses of avalanche formation preceding release and slab fracturing cases. The prominent AE attributes including βin-variations are also analysed at appropriate window scales for a smooth representation of the ongoing instability and failure processes underneath the slab. The AE spectral analyses of avalanche release and slab fracturing reveal the dominant frequencies around 45–47 kHz and 34–43 kHz, respectively. A progressively increasing behaviour was observed with an abrupt increase in the relevant AE parameters and also the βin-values, nearly 13 hours before the avalanche release, and it continued until the avalanche released which can be associated to the instability development prior to avalanche release. [ABSTRACT FROM AUTHOR]

Ultra-High-Performance Concrete (UHPC) was renowned for its exceptional mechanical strength and durability. While contemporary UHPC is made using high-quality materials and equipment, its enormous use of natural resources in construction has had global ecological impacts. Employing materials that are accessible locally without specialized procedures. This study concentrates on UHPC mixtures, formulated with indigenous materials, aiming to develop an economical product without the need for high-speed mixers or special fibers. The mixtures are designed with optimal proportions of various supplementary cementitious materials (SCM's). The study begins with an analysis of flow behaviour, followed by an evaluation of mechanical properties using non-destructive techniques (NDT) under different curing conditions. The UHPC mixtures demonstrate compressive strengths from 70–85 MPa to 110–144 MPa under standard and hot water curing. Relationships between destructive and NDT test outcomes are also determined. The hot water curing process leads to the formation of calcium-silicate-hydrate(C – S – H) gel, with calcium-hydroxide (CH) crystals observed within the amorphous C – S – H gel. The use of high-temperature curing improves mechanical characteristics by (35–40%) and densifies the microstructure. Consequently, producing UHPC using widely accessible materials without further processing and common technologies like conventional casting and standard curing would increase its cost-effectiveness and environmental sustainability. [ABSTRACT FROM AUTHOR]

In this study, the properties and structures of 304 austenitic stainless steels produced by investment casting methods and stages through destructive and non-destructive (NDT) testing methods are examined. First, the model mould was created from recycled wax containing 5–25% water, 15–35% filler and 0.5% ceramic for the production of a water pipe armature by investment casting method. Then, the ceramic coated mould was sintered at 1350°C, and 304 austenitic stainless steel was melted in the induction furnace at 1150°C at a speed of 1.36 kg/min and poured into this mould. The hardness value of the 304 austenitic stainless steel water pipe armature was obtained as 86.16 HRB and the tensile strength value was 518.76 MPa. As a result of examination with an optical metal microscope, ferrite and austenite structures were observed. Linear and round defects were detected in the liquid penetrant test, and third level (RT-3) shrinkage-delta defects were detected in the radiography test. These results showed that the armature part was acceptable in terms of mechanical strength, corrosion and leakage. [ABSTRACT FROM AUTHOR]

Terahertz (THz) non-destructive testing can detect internal defects in dielectric materials. However, this technology is mainly used for detecting thin and simple structures at present, lacking validations for the detection effectiveness of internal defects in thicker and more complex structures, such as fiber-web-reinforced composite sandwich panels. In this study, samples of fiber-web-reinforced polymethacrylimide foam sandwich panels, which are, respectively, 20 mm and 30 mm thick, were made to detect the internal debonding, inclusion, pore, and crack defects by the THz time-domain spectroscopy system (THz-TDS). The peak-to-peak-imaging algorithm, maximum-amplitude-imaging algorithm, minimum-amplitude-imaging algorithm, pulse-width-imaging algorithm, and time-of-flight-imaging algorithm were used to process and image the collected THz signals. The results showed that the peak-to-peak-imaging algorithm had the best performance. To address the low imaging resolution of THz-TDS, a block-based super-resolution reconstruction method—SSSRGAN—is proposed, which can improve image resolution while maintaining the clear edge contours of defects. The defect-detection results of the samples showed that THz-TDS could detect all pore, debonding, and crack defects, with a minimum size of 3 mm for pores and debonding and a minimum thickness of 1 mm for cracks. The method showed poor detection performance for inclusions with a thickness of 0.053 mm, but could still extract the defect features. Based on the THz-TDS reflection mode measurement principle, the thickness information of the panel, foam core, and web of the samples was calculated: the measurement error was no more than 0.870 mm for Sample #1 and no more than 0.270 mm for Sample #2, demonstrating the accuracy of THz-TDS in measuring the dimensions of sandwich panel structures. In general, THz technology shows potential for detecting internal defects and performing dimensional measurements in complex structures. With the advancement of portable devices and enhancements in detection speed, real-time on-site detection is anticipated in the future. [ABSTRACT FROM AUTHOR]

Adhesive joints are non-separable connections that are used in numerous ways in vehicle construction, particularly in buses. The widespread use of adhesive joints makes it necessary to assess their quality, especially under production conditions. The main goal of this study was to develop a mathematical model to estimate the width of the adhesive path in a plywood-adhesive-closed-profile joint based on selected parameters of the ultrasonic surface wave. A digital ultrasonic flaw detector and Rayleigh wave probes were applied. The test involved evaluating different widths of hybrid adhesive and two-component epoxy adhesive. The tests were conducted on a steel profile from a bus construction. The attenuation of the ultrasonic waves on the steel profile (0.026 db/mm) and the adhesive (0.264 dB/mm) was determined. A one-size-fits-all model for estimating adhesive path width for specific conditions is proposed. [ABSTRACT FROM AUTHOR]

Diffusion bonding technology is widely used in the connection of precision components, yet accurately and reliably detecting contact-type defects on the bond interface still remains a significant problem. Nonlinear ultrasonic methods have been proven to be sensitive to contact-type defects; however, the use of continuous wave or tone burst wave excitation limits its wider application. In this paper, dual-probe nonlinear ultrasonic testing with pulse wave excitation is proposed to detect contact-type defects in diffusion-bonded joints. A titanium alloy diffusion-bonded specimen with artificial defects was fabricated, and the corresponding detection device was designed based on the existing ultrasonic C-scan testing system. A C-scan imaging program based on nonlinear parameters was developed by extracting the fundamental and second harmonic waves of the reflection echo on the bond interface. The results demonstrated that the proposed detection scheme can obtain the nonlinear parameters of diffusion-bonded interfaces, and the nonlinear ultrasonic C-scan image of the bond interface is also obtained. The nonlinear parameter in the contact-type defects areas calculated from the bond interface echo is about 10 times (20 dB) higher than that in macro defects areas, whose gap is about 10 μm. The results indicate that the nonlinear ultrasonic methods seem to be more sensitive to contact-type defects and have a great potential to complement the insufficient detection capability of linear ultrasound for diffusion-bonded joints. [ABSTRACT FROM AUTHOR]

For CFRP-strengthened steel structures, corrosion defects in steel are incredibly hazardous, but there is a lack of effective non-destructive testing methods. Based on the low-power vibrothermography (LVT) technique, this study proposes a quantitative detection framework for corrosion defects of CFRP-strengthened steel structures, including feature extraction algorithms for improving the defect detectability, and a feature enhancement method for reducing the quantification error. First, a finite element model is built to simulate the heat generation under ultrasonic excitation and to obtain the temperature distribution on the specimen surface. The processing results of the simulated thermal images show that feature extraction algorithms (FFT, PCA, and PLSR) effectively extract defect features. Subsequently, the LVT experimental setup was established to detect corrosion defects in CFRP-strengthened steel specimens. The experimental results demonstrate that the LVT technique can selectively heat corrosion defects, and processing results prove that the FFT and PLSR can further enhance the defect detectability. Finally, we propose a feature enhancement method based on gravitational calculation and double-threshold segmentation to reduces the quantification error. The quantitative evaluation relative errors of defects with diameters of 10 mm, 8 mm, and 6 mm are 5.26%, 5.26%, and 10.09%, respectively. [ABSTRACT FROM AUTHOR]

Assessment of the historic bearings of the Chemnitz viaduct The Chemnitz viaduct was built at the beginning of the 20th century and has been refurbished in recent years in line with its listed status. This required, among other things, an assessment of the reuse of the bearings consisting of individual cast components. The assessment included standard non‐destructive and destructive testing as well as numerical calculations to verify the load‐bearing capacity. As a novel addition, casting simulations, 3D scans of the bearings to record the actual condition and fracture mechanical investigations to assess the risk of brittle fracture and possible crack propagation were carried out. Among other things, difficult accessibility and complex load conditions posed a challenge. This paper presents the bearing concept and the methodology of the assessment and describes the individual components with the associated results. The extensive assessment allowed for the continued use of most of the bearing components. [ABSTRACT FROM AUTHOR]

Detecting the low-velocity impact damage on the FRP composite structures can be challenging because the visible signs on the surface may be subtle. Mitigating the effects of BVID (Barely Visible Impact Damage) is crucial in ensuring the safety and reliability of composite components, especially in industries where these materials are commonly used, such as aerospace and automotive. This article explores the experimental investigations of the impact and post-impact damage propagation under compression in the Graphene Nano-Platelets (GNPs) infused glass fiber epoxy composite laminates. A comparative study of varying the percentage of the nano-platelets (0, 0.25, 0.5, and 0.75%) in the matrix material of the composite laminate is presented. The impact tests at low energies (15 J, 20 J, and 25 J), followed by the quasi-static compression tests, are carried out as per the procedures mentioned in the ASTM D7136/D7137. The digital image correlation technique (DIC) is used to measure strains and plot the strain field on the impacted face of the specimen under compression. Damage envelopes of the impact and compression tests are studied using X-ray CT scans. Micro-level failures are observed through Scanning Electron Microscopy (SEM) to get better insights into the failure mechanism of the composite materials under impact and compression loadings. Upon completing the study, it is found that the addition of the GNP into the matrix materials not only increased the impact performance but also increased the load-carrying capacity of the laminate under compressive loading. A decrement of 2–21% in the energy absorbed in the damage formation is seen with the incorporation of GNPs into composite materials. A maximum increment of 17% in the compressive strength is observed for the composite laminates with the 0.75% GNPs. However, for a particular percentage of the GNP, the compressive strength of the laminate is reduced with an increase in impact energy compared with its virgin counterpart. From X-ray CT scans, it is seen that the laminate under the compression tests fails due to excessive ply-splitting and the delamination at the damage site created during the impact. Fiber breakage, fiber brooming, matrix cracking, fiber-matrix debonding, etc., are some of the other failure modes observed in SEM studies. [ABSTRACT FROM AUTHOR]

Small-diameter stainless steel pipes find widespread application in nuclear power systems. In order to detect surface axial wear defects that may occur during their service life, a circular fully enclosed probe has been developed for inspecting these small-diameter stainless steel pipes. The probe is composed of a periodic permanent magnet (PPM) and a racetrack-shaped coil. Through the optimised design of the PPM structure, two probes were devised to excite single flexural and single torsional modes, respectively. Furthermore, employing the non-equivalent concept, the torsional mode guided wave probe underwent optimisation, resulting in a 54% increase in the received signal amplitude. Finally, two types of guided wave probes were utilised to conduct detection experiments on artificially induced defects in an outer diameter 10 mm sample pipe. The results indicate that both T(0,1) and F(1,2) mode guided waves can detect axial defects. Moreover, the detection amplitude of the T(0,1) mode guided wave surpasses that of the F(1,2) mode guided wave by a maximum of 53%. [ABSTRACT FROM AUTHOR]

β -detected nuclear magnetic resonance (β -NMR) is a powerful research tool in nuclear physics and material sciences. In this study, we integrated an imaging function into a β -NMR instrument. Specifically, we installed position-sensitive detectors to create a magnetic resonance imaging device, which we have named the β -MRI. We conducted experiments to evaluate the performance of this device by irradiating spin-polarized 12 B beams on a sample, and successfully generated its material mapping image. [ABSTRACT FROM AUTHOR]

Water has engaged in a series of activities during plant growth. Therefore, the moisture content is one of the most important indicators of plant growth. Water deficiency can lead to a decrease in the stomatal degree on the leaf surface, where the carbon dioxide can flow into the plant leaf. The resulting photosynthetic efficiency can greatly reduce the appearance, nutrition, and total biomass of forestry plants. However, it is still lacking in the rapid detection of moisture content in plants. In this study, a polarization imaging system was proposed using a split-focal plane polarization camera. Polarization images were then analyzed to establish the dependence between the linear polarization and moisture content, in order to reveal the interaction between polarized light and water in plants. The rapid and nondestructive detection was realized for the moisture content of wintergreen leaves. The specific research was as follows. 1) The holly leaves were first collected and dried in a gradient manner. The moisture content was obtained after weighing; 2) A sub-focal plane polarization system was developed to detect the polarization of the gradient-dried holly leaves; 3) A systematic investigation was carried out to clarify the influence of growth states on the moisture content of holly leaves under various months, different parts, as well as sunny or rainy weather conditions. 40 leaves were collected and then divided into 8 groups, with 5 leaves for the control in each group; 4) The holly leaves were detected for the polarization. Stokes parameters were obtained to convert into the linear polarization degrees with clear physical significance and imaging. It was found that the linear polarization degree increased gradually, as the moisture content decreased. There was a strong negative correlation between the leaf moisture content and the linear polarization degree. 5) Rapid detection of moisture content was achieved by the quantitative relationship between moisture content and linear polarization degree after fitting. The degree of linear polarization was only used to detect the moisture content of the target leaf. 6) Other holly leaves were collected and then substituted into the fitting model, in order to verify the prediction of the model. The results show that the moisture content of holly spear leaves was represented by the linear polarization degree in the three growth conditions (months, weather, and parts). Furthermore, the linear polarization degree increased gradually with the decrease in the gradient of moisture content. A prediction model was obtained using the large number of plant leaves. The correlation coefficient reached 0.85, and the MSE was only 0.45%. Verification through the leaf moisture detection system showed that the average relative error of the moisture detection system was 9.50%, and the RMSE was 4.53%. The true moisture content of the leaf was characterized by linear polarization degree. The findings can also provide optical applications in plant health detection. [ABSTRACT FROM AUTHOR]

This study analyses a cold-forged gear blank failure that was reported by the industry. In cold forged metal, the damage is seen as fissures running parallel to the thickness direction. Visual inspection, composition analysis, hardness, microstructure, residual stress measurement, and non-destructive testing like magnetic particle inspection, ultrasonic detection, and radiographic tests were performed and analyzed to investigate the problem. The thorough research found that the crack appeared during the material's evolution's earliest stages and acceptance standards. The same has been discussed with the end user to ascertain that this type of failure should not occur again. The objectives, methods, and anticipated results of the study are briefly summarised in the abstract. The project intends to provide significant insights to the field of mechanical engineering, leading future developments in gear design, manufacture, and performance optimisation by a thorough analysis of the failure evaluation of a gear blank. The significance of this investigation is due to its ability to improve the design and production of gear blanks and related components. Engineers and designers can improve the material choice, manufacturing processes, and operational circumstances by identifying the primary reasons of failure and comprehending the crucial aspects that caused the malfunction. As a result, gear systems may perform better, last longer, and be more reliable in a variety of applications. [ABSTRACT FROM AUTHOR]

Bridges are critical components of transportation infrastructure, connecting communities and facilitating the movement of goods and people. Real-time monitoring of structural states of bridges can provide reasonable guidance for bridge maintenance. To reduce maintenance costs and extend the lifespan of bridges, a digital twin-based non-destructive testing (NDT) method for the structural health monitoring of bridges is proposed. The digital twin-based NDT method is realised by the combination of three-dimensional modelling technology, sensor technology, finite element (FE) method, and surrogate models. The main advantage of the proposed method is that each node of the FE model can be regarded as a virtual sensor to reflect the structural performance of the bridge. In other words, the structural performance of the entire bridge can be monitored by the digital twin-based NDT method. In addition, the digital twin-based NDT method integrates the advantages of FE models that can provide a causal relationship in structural analysis and the characteristics of surrogate models that are easy to build and less costly. The feasibility and effectiveness of the proposed method is demonstrated by a suspension bridge. [ABSTRACT FROM AUTHOR]

To date, carbon utilization within the concrete industry is predominantly revolved around gaseous and liquid carbon dioxide (CO2), with relatively limited research dedicated to the application of solid CO2. This study was the first to use solid CO2 (dry ice) as an additive in alkali-activated slag and fly ash (AASF) to investigate its influences on the alkali-activation process and various property improvements. Two study systems were designed: one with alkali-activated slag (AAS) as the control group and fly ash (FA) added at substitution rates of 10%, 30%, and 50%; and the other with 50% FA alkali-activated materials (AAM) as the control group and dry ice added at substitution rates of 4%, 8%, and 12%. Several tests were conducted on AASF, encompassing assessments of setting time, compressive strength, and durability (electrical resistivity and chloride ion diffusion coefficient). Furthermore, a comprehensive analysis of its microstructure was undertaken, employing analytical techniques, such as X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and thermogravimetric analysis (TGA). The results showed that FA substitution effectively mitigated the short setting time of AAS; however, the mechanical properties and durability decreased by approximately 50%. When dry ice was added to AAM concrete, it induced a series of physical and chemical effects that altered the kinetics of the hydration reaction of AAM. Specifically, adding dry ice significantly prolonged the setting time of AAMs. Moreover, this addition did not damage the mechanical properties or durability of AAS(F). [ABSTRACT FROM AUTHOR]

Estimating the axial force of rock bolts is vital for the stability and safety of underground excavations. Current methods suffer from being labor-intensive, time-consuming, and prone to inaccuracies. This paper presents a novel method for estimating the axial force of a rock bolt, a critical element in underground engineering, by analyzing the deformation field of the bearing plate. This innovative approach combines 3D laser scanning for data acquisition with a developed convolutional neural network (CNN) model for data processing, resulting in an efficient and non-destructive external monitoring technique. The proposed method was verified with 27 laboratory experiments. The results showed an accurate estimation of the axial forces of rock bolts with an average error of ± 5 kN, underlining the method's potential for in-situ application in underground engineering. This research not only contributes to the development of intelligent systems in rock mechanics and engineering but also has significant implications for industries, such as mining, tunneling, and underground engineering. Highlights: The study introduces a novel method that utilizes 3D laser scanning and a developed CNN model to accurately estimate the axial force of rock bolts. The proposed method achieves accurate estimations of rock bolts' axial forces with an average error of ± 5 kN. Future work includes the integration of object detection algorithms for large-scale 3D laser scanning and the creation of a database of various bearing plates to enhance the method's robustness. [ABSTRACT FROM AUTHOR]

The viceroyal ecclesiastical architecture of the Puno region in Peru is the legacy of the evangelisation in the western part of Lake Titicaca. In recent years, the preservation of this heritage has been threatened by various risks including planar deformation and cracking. The objective of the study presented here is to determine the level of threat in terms of risk and to understand the pathologic agents that affect the conservation of these important facades. The methodology used focussed on non-destructive techniques, including visual inspection, digital image processing and thermography, to identify the processes affecting the facades. Multidisciplinary analysis using geographic information systems facilitated the mapping of naturally-induced risks, while multidimensional evaluation using qualitative techniques, such as consultation with experts and surveys, helped determine the level of anthropic risk. The results generally demonstrate a low to medium level of risk to the facades but suggest a higher incidence of pathologies of a physical origin. Results of the study help establish what are the critical threats to the conservation of built heritage in the Andes region and represent a contribution to conservation management by demonstrating the use of non-destructive techniques for damage assessment. [ABSTRACT FROM AUTHOR]

Background: Active infrared thermography is proved to be viable and attractive for non-destructive evaluation of interfacial defects like delaminations in a coating-substrate system. But it is a challenging task to accurately quantify small and deeply buried defects from thermal images, due to the inevitable effects of lateral heat diffusion and measurement noise. Objective: The aim of this work is to estimate the size and pattern of defects at the interface of a two-layer system with high accuracy and high reliability based on step heating thermography. Methods: To characterize the effect of defect on the heat flow, a virtual heat flux is assumed at the interface, which is reconstructed from measured surface temperature by solving a three-dimensional inverse problem. The inverse solution is obtained using the Green's function and regularization techniques, and then used for estimating the defect pattern by threshold segmentation. An improvement on computational efficiency is achieved by an iteratively substitution of nodal temperature. Results: Simulations with synthetic data generated by a finite element model validate the feasibility of this approach. Results obtained from experiments for an Aluminum oxide/steel system show the robustness of this approach, when temperatures are contaminated with measurement noise. Both the performance on estimation of various defect shapes and the effects of regularization are discussed. Conclusion: This study show that the present approach brings an improvement in accuracy and reliability for the estimation of size and pattern of defects with various diameter-to-depth ratios, in comparison with conventional techniques. [ABSTRACT FROM AUTHOR]

The ultrasonic pitch–catch method was used to study the hydration behaviour of Portland cement paste with sodium gluconate (SG) additive using a tailor-made piezoelectric transducer. X-ray diffraction analysis showed that the intensity of the diffraction peak of calcium hydroxide decreased with increasing SG content (CSG), especially for CSG > 0.10 wt%. The compressive strength of the hardening cement pastes decreased remarkably for the pastes with CSG > 0.05 wt%. Ultrasonic waveforms appeared after 2 h of hydration and the waveform amplitudes of cement pastes with SG contents of 0.15 wt% and 0.20 wt% were obviously larger than those of cement pastes with CSG = 0.05 wt% in the early hydration stage. The hydration process of the cement paste over 300 h was classified into three stages based on the head wave amplitude variation – the fluctuation period, the rapidly increasing period and the smoothly increasing period. The acoustic propagation time changed greatly with hydration before a hydration age of 48 h, indicating that the hydration and hardening process of the cement paste was intense in the first 48 h. The proposed ultrasonic monitoring method opens a new perspective for studying the hydration behaviour of SG-blended cement paste. [ABSTRACT FROM AUTHOR]

During production or service life, lightweight components made of fibre-reinforced polymers (FRP) may suffer from different types of defects such as cracks or delamination, which may be detected, amongst others, with the help of ultrasonic lamb waves (LW). The study intends to illustrate the potentials of numerical models to assist the design of LW-based non-destructive testing (NDT) processes. For that, the complete NDT chain of an LW driven inspection system was supported by a Finite Element Analysis (FEA) using stringer-stiffened C FRP panels as an exemplary component. The study was divided in two publications: The first part focused on the description of the numerical model, illustration of its functionalities and – most importantly – the determination of LW related effects that were needed to design the inspection system (dispersion characteristics, excitation frequency, propagating modes etc.). In this part, the FEA driven design approach was first validated with self measured experimental data as well as experiments provided in literature. In the second step, the numerical model was applied for the performance of parameter studies in order to identify component specific aspects that proved important for the NDT process. Finally, general advices and FEA related working steps were derived from all findings to assist practitioners in the implementation of the technique. [ABSTRACT FROM AUTHOR]

When using near‐infrared (NIR) techniques for analysis, model construction and maintenance updates are essential. When model construction is performed in machine learning, the sample set is usually divided into the calibration set and the validation set. The representativeness of the calibration set and the reasonable distribution of the validation set affects the accuracy of the established model. In addition, when maintaining and updating models, selecting the most informative updated sample not only improves the model prediction accuracy but also reduces sample preparation. In this paper, the spectral information entropy (SIE) is proposed to be used as a similarity criterion for dividing the sample set and use this criterion to select updated samples. The Kennard–Stone (KS) and the sample set portioning based on joint x–y distance (SPXY) methods were used for comparison to verify the superiority of the proposed method. The results showed that the model built after dividing the sample set using the SIE method has good prediction effect compared with KS and SPXY method. When predicting soluble solid content (SSC) and hardness, the prediction determination coefficient (RP2) was improved by more than 15%, and the root mean square error (RMSE) of prediction was reduced by 50%. In terms of model updating, selecting a small number of updated samples using the SIE method can improve the correlation coefficient (RP) to more than 80%, and updated models' prediction accuracy is higher than that of KS and SPXY method. It is confirmed that the SIE method can make the NIR analysis technique more reliable. [ABSTRACT FROM AUTHOR]

The most common methods for texture characterization require cutting a coupon to be inserted into an X‐ray texture goniometer or an electron microscope and are, therefore, destructive. However, there are many cases where texture characterization needs to be performed in a non‐destructive way, where the measurement can be made without damaging the object. Typical examples are archaeological artefacts or very expensive materials. Here we demonstrate the application of an X‐ray diffractometer mounted on a robotic arm designed for residual stress scanning for texture characterization, enabling the determination of the orientation distribution function for different locations on samples with complex geometries. The texture characterization method was benchmarked on a cold‐rolled aluminium sheet. [ABSTRACT FROM AUTHOR]

Non-destructive ultrasound phase spectroscopy (UPS) was used to study the influence of thermal shock on the longitudinal elastic constant (C 11) of ZrB 2 -B 4 C composites. C 11 of the composites was measured in the as-fabricated condition and after different numbers of thermal shock cycles. The relative change in C 11 was used as the thermal shock resistance parameter. Composites with 70 and 50 vol% ZrB 2 were subjected to three thermal shock temperature differences (ΔT) of 600 °C, 800 °C, and 1000 °C, respectively. The rate of drop in C 11 increased significantly with an increase in ΔT and B 4 C content. The mechanism of thermal shock damage has been discussed considering the influence of processing-induced thermal residual stresses and the average stress state in both phases during thermal shock. It was shown that the powder particle arrangement plays a major role in the damage mechanism and B 4 C-B 4 C particle contacts provide easy paths for crack propagation. [ABSTRACT FROM AUTHOR]

Terahertz tomography is a promising method among non-destructive inspection techniques to detect faults and defects in dielectric samples. Recently, image quality was improved significantly through the incorporation of a priori information and off-axis data. However, this improvement has come at the cost of increased measurement time. To aim toward industrial applications, it is therefore necessary to speed up the measurement by parallelizing the data acquisition employing multi-channel setups. In this work, we present two tomographic frequency-modulated continuous wave (FMCW) systems working at a bandwidth of 230–320 GHz, equipped with an eight-channel detector array, and we compare their imaging results with those of a single-pixel setup. While in the first system the additional channels are used exclusively to detect radiation refracted by the sample, the second system features an f-θ lens, focusing the beam at different positions on its flat focal plane, and thus utilizing the whole detector array directly. The usage of the f-θ lens in combination with a scanning mirror eliminates the necessity of the formerly used slow translation of a single-pixel transmitter. This opens up the potential for a significant increase in acquisition speed, in our case by a factor of four to five, respectively. [ABSTRACT FROM AUTHOR]

Finite element analysis (FEA) was used in this research to determine the dynamic behaviour of thermally modified Norway spruce (Picea abies Karst.). Damped modal analysis was selected for this purpose. Data obtained from non-destructive experimental work and from the literature provided input for modal analysis. The material characteristics were optimised and validated by experimental measurement; then the validated material model was mathematically described and FEA was applied. This work resulted in a generally validated material model of modified spruce based on material constants at various modification temperatures and humidities. Good agreement with a relative error of up to 7.1% was found between the numerical model and the experimental data. It was found that thermal modification caused an increase in resonance frequencies and higher humidity caused a decrease. The data provided and the model could prove useful for the non-destructive evaluation of thermally modified spruce and for predicting the behaviour of materials used in making musical instruments. [ABSTRACT FROM AUTHOR]

Purpose. Find out the possibilities of using acoustic and radiographic methods of non-destructive testing to detect defects in products of electron beam surfacing made of titanium, steel, tungsten, molybdenum and bronze, as well as to determine optimal technologies and control parameters. Methodology. Comparative control of the acoustic research was carried out by contact transducers, longitudinal (compression) ultrasonic waves of the following frequencies/diameters of the working surface: 2 MHz/24 mm; 2.5 MHz/10mm and 5 MHz/14mm. The radiographic examination was carried out using the technology of classic film radiography and DR (digital direct radiography). Findings. It is shown that ultrasonic and radiation control methods allow to assess the quality of products of additive technologies in accordance with regulatory documents. Originality. Based on the research results, recommendations were made regarding the use of non- destructive testing methods to detect defects and mechanical properties of finished products. Practical value. The use of modern ultrasonic flaw detectors with phased antenna arrays (PHAs), with the simultaneous use of TFM (Total Focusing Method) and FMC (Full Matrix Capture) modes to detect defects of arbitrary orientation located at different depths is promising for researching the quality of products of additive technologies. [ABSTRACT FROM AUTHOR]

Industrial radiography is a pivotal non-destructive testing (NDT) method that ensures quality and safety in a wide range of industrial sectors. Conventional human-based approaches, however, are prone to challenges in defect detection accuracy and efficiency, primarily due to the high inspection demand from manufacturing industries with high production throughput. To solve this challenge, numerous computer-based alternatives have been developed, including Automated Defect Recognition (ADR) using deep learning algorithms. At the core of training, these algorithms demand large volumes of data that should be representative of real-world cases. However, the availability of digital X-ray radiography data for open research is limited by non-disclosure contractual terms in the industry. This study presents a pipeline that is capable of modeling synthetic images based on statistical information acquired from X-ray intensity distribution from real digital X-ray radiography images. Through meticulous analysis of the intensity distribution in digital X-ray images, the unique statistical patterns associated with the exposure conditions used during image acquisition, type of component, thickness variations, beam divergence, anode heel effect, etc., are extracted. The realized synthetic images were utilized to train deep learning models, yielding an impressive model performance with a mean intersection over union (IoU) of 0.93 and a mean dice coefficient of 0.96 on real unseen digital X-ray radiography images. This methodology is scalable and adaptable, making it suitable for diverse industrial applications. [ABSTRACT FROM AUTHOR]

Chlorophyll content is highly susceptible to environmental changes, and monitoring these changes can be a crucial tool for optimizing crop management and providing a foundation for research in plant physiology and ecology. This is expected to deepen our scientific understanding of plant ecological adaptation mechanisms, offer a basis for improving agricultural production, and contribute to ecosystem management. This study involved the collection of hyperspectral data, image data, and SPAD data from jujube leaves. These data were then processed using SG smoothing and the isolated forest algorithm, following which eigenvalues were extracted using a combination of Pearson's phase relationship method and the Partial Least Squares Regression–continuous projection method. Subsequently, seven methods were employed to analyze the results, with hyperspectral data and color channel data used as independent variables in separate experiments. The findings indicated that the integrated BPNN-RF-Ridge Regression algorithm provided the best results, with an R2 of 0.8249, MAE of 2.437, and RMSE of 2.9724. The inclusion of color channel data as an independent variable led to a 3.2% improvement in R2, with MAE and RMSE increasing by 1.6% and 3.9%, respectively. These results demonstrate the effectiveness of integrated methods for the determination of chlorophyll content in jujube leaves and underscore the potential of using multi-source data to improve the model fit with a minimal impact on errors. Further research is warranted to explore the application of these findings in precision agriculture for jujube yield optimization and income-related endeavors, as well as to provide insights for similar studies in other plant species. [ABSTRACT FROM AUTHOR]

Real-time evaluation of the damage location and level of rock mass is essential for preventing underground engineering disasters. However, the heterogeneity of rock mass, which results from the presence of layered rock media, faults, and pores, makes it difficult to characterize the damage evolution accurately in real time. To address this issue, an improved method for rock damage characterization is proposed. This method optimizes the solution of the global shortest acoustic wave propagation path in the medium and verifies it with layered and defective media models. Based on this, the relationship between the inversion results of the wave velocity field and the distribution of rock damage is established, thereby achieving quantitative characterization of rock damage distribution and degree. Thus, the improved method is more suitable for heterogeneous rock media. Finally, the proposed method was used to characterize the damage distribution evolution process of rock media during uniaxial compression experiments. The obtained results were compared and analyzed with digital speckle patterns, and the influencing factors during the use of the proposed method are discussed. [ABSTRACT FROM AUTHOR]

Moldy heart disease is one of the most serious diseases in apples. However, there are hidden symptoms of fruit heart disease in the early and middle stages. The mixing of healthy apples has posed a great risk to the overall quality of apples, resulting in the healthy apples rotting together. It is very urgent for rapid and nondestructive testing on the moldy heart disease in apples. In this study, near-infrared spectroscopy was used to rapidly and non-destructively detect the mold heart disease with higher precision in apples. The influence of the light source spot diameter on the detection of mold heart disease in apples was explored, in terms of both spectra and classification models. The 127 red Fuji apple samples were selected in the experiment, which was purchased in March 2023 in Luochuan County, Yan'an City, Shanxi Province. The transmission spectra of apple samples were collected using a near-infrared spectrometer (NIRS) with a wavelength range of 200-1 100 nm at light source spot diameters of 30, 50, and 70 mm. A systematic analysis was first made to determine spectral differences between healthy and mold heart apples under different light source spot diameters. Then, the classification models were established using support vector machines (SVM) and particle swarm optimization-least squares support vector machine (PSO-LSSVM) combined with different pre-processing methods. The performance of classification models was also compared under different light source spot diameters. Competitive adaptive reweighted sampling (CARS) was used to screen the characteristic wavelength variables, in order to optimize the classification model. The results show that the spot diameter of a 30 mm light source had the best effect on the detection of mold heart disease in apples, indicating the best performance of SVM and PSO-LSSVM classification models. Specifically, the performance of classification models decreased, as the spot diameter of the light source increased. Meanwhile, the path of the light source mainly carried the information around the apple core at the spot diameter of 30 mm. The spectra collected from this region also shared more information about the diseased tissue of apples. By contrast, the path of the light source covered most or all of the apple at the spot diameters of 50 or 70 mm. More information was then collected about the healthy apple tissues, leading to interference with the detection of mold heart disease in apples. The Sensitivity, Specificity, and Accuracy of the best PSO-LSSVM were 89.5%, 95.5%, and 92.7%, respectively, in the prediction set at 30 mm light source spot diameter. The CARS effectively screened the feature wavelengths. The performance of the CARS-PSOLSSVM classification model was slightly inferior to that of the full-wavelength model, where the Sensitivity, Specificity, and Accuracy in the prediction set were 89.5%, 90.9%, and 90.2%, respectively. Therefore, the classification model was effectively simplified with high recognition accuracy and strong robustness, although the number of modelled variables was only 4.2% of the original. This finding can provide the technical support for the rapid, non-destructive, and high-precision detection of apple mold heart disease. [ABSTRACT FROM AUTHOR]

Road agencies are looking for efficient pavement management tools to maintain adequate service levels and preserve the asset value. The traffic speed deflectometer (TSD) allows to continuously assess the structural condition of the pavement while moving at traffic speeds, thus overcoming the typical drawbacks of traditional non-destructive testing equipment. However, managing the huge amount of data deriving from TSD surveys is challenging, and there is no established method to use those data for identifying the remaining service life (RSL) of the pavement network. This study aimed at developing a new practical and reliable methodology to assess the RSL of motorway pavements at the network level from TSD measurements. The methodology is based on the use of the surface curvature index SCI300 and includes the temperature adjustment, the definition of a representative SCI300 value for each pavement section and the estimation of the residual fatigue resistance (considering bottom-up cracking as the critical distress). The methodology was implemented using the deflection data deriving from a TSD survey carried out on about 400 km of motorway pavement. The obtained RSL predictions can be used to identify the structural ranking of different pavement sections and guide budget forecasting, according to the actual maintenance and rehabilitation priorities. [ABSTRACT FROM AUTHOR]

This article presents a method of ultrasonic testing (UT) that detects and quantifies interlaminar delaminations in CFRP composites with high resolution in terms of both spatial resolution in the planar dimension and depth into the laminate. Unidirectional and woven CFRP laminates were fabricated for this study, with a PTFE film inserted at various depths throughout the laminate to act as intentional crack initiation sites. All samples were mechanically tested via a three-point, end-notched flexure (ENF) test, followed by a quantification of the extent of the induced interlaminar delaminations using UT and X-ray computed tomography (CT). UT analysis for unidirectional CFRP samples was able to show a clear contrast between the delaminated area and the non-delaminated area. UT analysis of the woven CFRP samples yielded comparable results but required finer tuning of analysis parameters due to the interlocking woven fabric. CT results revealed a significant contrast between the crack and composite; thus, fine geometrical features of the crack front could be observed. UT and CT measurements were then compared, revealing an average difference of 1.09% in the delamination area, with UT overestimating as compared to CT. A UT depth study was also performed to automatically locate the interlaminar delamination at different depths throughout the components, with the delamination being predicted within one lamina interface for all samples. These results demonstrate UT's ability to accurately detect and quantify the extent and location of interlaminar delaminations due to bending. [ABSTRACT FROM AUTHOR]

Sewage and water networks are crucial infrastructures of modern urban society. The uninterrupted functionality of these networks is paramount, necessitating regular maintenance and rehabilitation. In densely populated urban areas, trenchless methods, particularly those employing cured-in-place pipe technology, have emerged as the most cost-efficient approach for network rehabilitation. Common diagnostic methods for assessing pipe conditions, whether original or retrofitted with-cured-in-place pipes, typically include camera examination or laser scans, and are limited in material characterization. This study introduces three innovative methods for characterizing critical aspects of pipe conditions. The impact-echo method, ground-penetrating radar, and impedance spectroscopy address the challenges posed by polymer liners and offer enhanced accuracy in defect detection. These methods enable the characterization of delamination, identification of caverns behind cured-in-place pipes, and evaluation of overall pipe health. A machine learning algorithm using deep learning on images acquired from impact-echo signals using continuous wavelet transformation is presented to characterize defects. The aim is to compare traditional machine learning and deep learning methods to characterize selected pipe defects. The measurement conducted with ground-penetrating radar is depicted, employing a heuristic algorithm to estimate caverns behind the tested polymer composites. This study also presents results obtained through impedance spectroscopy, employed to characterize the delamination of polymer liners caused by uneven curing. A comparative analysis of these methods is conducted, assessing the accuracy by comparing the known positions of defects with their predicted characteristics based on laboratory measurements. [ABSTRACT FROM AUTHOR]

The processes of heat removal from multijunction solar cells on a germanium substrate to AlN ceramics through a solder layer were studied by thermal wave photodeflection methods. Two types lead-free solder based on SnBi and SnAgCu were used under various pressure soldering conditions. The thermal conductivity and thermal resistance of the solder layers are compared. It is shown that the thermal conductivities of the solder layers differ from the reference data for the corresponding metal alloys, and in some cases may depend on the pressure during the brazing process. [ABSTRACT FROM AUTHOR]

Rapid and non-destructive testing is of great significance for the quality grading in the citrus industry. This study was focused on the actual needs of on-site real-time detection on the internal quality of citrus fruits with different fruit diameters (25-95mm) in China. The spectral variation coefficient of citrus fruits was determined with different fruit diameters at the same point. A systematic investigation was made to explore the impact of the incident light angle on the spectral information of citrus fruits. A dual-gear probe was then designed for the local diffuse transmission spectral acquisition. A portable non-destructive testing device was developed for the internal quality of citrus fruits with multiple fruit diameters. Four modules included a light source, a spectrum acquisition, a control and display, as well as a power supply module. The size of entire device was 160mm × 80mm × 65mm, weighing 390g. Taking sugar orange (fruit diameter 25.35-48.61mm) and Wuming Wo orange (fruit diameter 53.24-94.71mm) as the research objects, the spectra were collected every 120 ° at the equator using the dual range probe, and then the average spectrum was used as the original spectrum of the citrus. Competitive adaptive weighted sampling (CARS) was used to filter feature wavelengths after preprocessing with standard normal variable transformation (SNV) and multiple scatter correction (MSC). A partial least squares (PLS) prediction model was established for the soluble solids content (SSC) and moisture content of citrus. The correlation coefficients of the SSC and moisture prediction model validation set for Wogan were 0.937 and 0.951, respectively, with the root mean square errors (RMSE) of 0.382°Brix and 0.491%, respectively; The correlation coefficients of SSC and moisture prediction model validation set for sugar orange were 0.921 and 0.935, respectively, with the RMSE of 0.460°Brix and 0.673%, respectively. A real-time analysis and control software was written for the device using C/C++language in the QT development tool. One click, real-time, and nondestructive testing was achieved in the multiple qualities of citrus fruits with different diameters. Finally, the experimental verification was conducted on the stability and accuracy of the developed device. The results showed that the average coefficient of variation of SSC and water content predicted by the device for Wogan were 0.024% and 0.022%, respectively; The average coefficient of variation (CV) of SSC content and water content in sugar orange were 0.014% and 0.020%, respectively; The maximum absolute residual values of SSC and moisture device predictions and standard physicochemical values for Wogan were 1.15% and 1.37%, respectively, while those of sugar orange were 1.71% and 1.58%, respectively. Therefore, the portable dual-gear device can fully meet the requirements of on-site and real-time spectral detection on the multiple quality of citrus, particularly for the stability and accuracy of internal quality detection of citrus fruits with different fruit diameters. [ABSTRACT FROM AUTHOR]