Search Results (1,596)

Post-tensioned (PT) reinforced concrete bridges are particularly vulnerable structures, as the deterioration of internal tendons is often difficult to detect using conventional inspection methods or visual assessments. This paper introduces a practical framework for ranking non-destructive testing (NDT) techniques employed to assess PT systems. The ranking is based on four performance categories: measurement accuracy, ease of use, cost, and impact of disruption to bridge operations on traffic. For each NDT technique, a score is assigned for each evaluation category, and the final ranking is determined using the weighted sum model (WSM). This approach enables the final assessment to reflect the priorities of different decision-making contexts defined by the end-user such as accuracy-oriented, cost-oriented, and impact-oriented scenarios. The proposed method is then applied to an existing bridge in order to practically demonstrate its effectiveness and the flexibility of the proposed criteria. Full article

In order to meet the needs for the rapid detection of crop growth and support variable management in farmland, an intelligent chlorophyll content in cotton leaves (CCC) detection system based on hyperspectral imaging (HSI) technology was designed and developed. The system includes a near-infrared (NIR) hyperspectral image acquisition module, a spectral extraction module, a main control processor module, a model acceleration module, a display module, and a power module, which are used to achieve rapid and non-destructive detection of chlorophyll content. Firstly, spectral images of cotton canopy leaves during the seedling, budding, and flowering-boll stages were collected, and the dataset was optimized using the first-order differential algorithm (1D) and Savitzky–Golay five-term quadratic smoothing (SG) algorithm. The results showed that SG had better processing performance. Secondly, the sparrow search algorithm optimized backpropagation neural network (SSA-BPNN) and one-dimensional convolutional neural network (1DCNN) algorithms were selected to establish a chlorophyll content detection model. The results showed that the determination coefficients $R_p^2$ of the chlorophyll SG-1DCNN detection model during the seedling, budding, and flowering-boll stages were 0.92, 0.97, and 0.95, respectively, and the model performance was superior to SG-SSA-BPNN. Therefore, the SG-1DCNN model was embedded into the detection system. Finally, a CCC intelligent detection system was developed using Python 3.12.3, MATLAB 2020b, and ENVI, and the system was subjected to application testing. The results showed that the average detection accuracy of the CCC intelligent detection system in the three stages was 98.522%, 99.132%, and 97.449%, respectively. Meanwhile, the average detection time for the samples is only 20.12 s. The research results can effectively solve the problem of detecting the nutritional status of cotton in the field environment, meet the real-time detection needs of the field environment, and provide solutions and technical support for the intelligent perception of crop production. Full article

This article explores the issue of surface modification through tumbling and vaporisation of 3D-printed materials, and its impact on the electrolytic deposition of metal coatings on previously non-conductive materials. Plastic materials represent an affordable alternative, but their surface treatment, in the form of post-coating, achieves properties comparable to those of metal parts while saving expensive metal material. Samples prepared by selective laser sintering (SLS) with different surface treatments were used. Polyamide 12 (PA12) was chosen as the base material and copper (Cu) as the metallic coating. Graphite was sprayed on the samples to ensure conductivity. The Cu coating was electrodeposited from an acidic copper electrolyte. The quantitative analysis of the surface was carried out using standard ISO parameters. The thickness of the deposited copper layer was determined using destructive measurements on a digital microscope. The results show that surface modification has a significant effect on the functional properties of the surface quality and the thickness of the deposited copper layer. Full article

The quality of wine largely depends on the quality of wine grapes, which is determined by their chemical composition. Therefore, measuring parameters related to grape ripeness, such as soluble solids content (SSC), is crucial for harvesting high-quality grapes. Visible–Near-Infrared (Vis/NIR) spectroscopy enables effective, non-destructive detection of SSC in grapes. However, commercial Vis/NIR spectrometers are often expensive, bulky, and power-consuming, making them unsuitable for on-site applications. This article integrated the AS7265X sensor to develop a low-cost handheld IoT multispectral detection device, which can collect 18 variables in the wavelength range of 410–940 nm. The data can be sent in real time to the cloud configuration, where it can be backed up and visualized. After simultaneously removing outliers detected by both Monte Carlo (MC) and principal component analysis (PCA) methods from the raw spectra, the SSC prediction model was established, resulting in an ${\mathrm{R}}_V^2$ of 0.697. Eight preprocessing methods were compared, among which moving average smoothing (MAS) and Savitzky–Golay smoothing (SGS) improved the ${\mathrm{R}}_V^2$ to 0.756 and 0.766, respectively. Subsequently, feature wavelengths were selected using UVE and SPA, reducing the number of variables from 18 to 5 and 6, respectively, further increasing the ${\mathrm{R}}_V^2$ to 0.809 and 0.795. The results indicate that spectral data optimization methods are effective and essential for improving the performance of SSC prediction models. The IoT Vis/NIR Spectroscopic System proposed in this study offers a miniaturized, low-cost, and practical solution for SSC detection in wine grapes. Full article

This study introduces and validates a comparative experiment-based method for determining the effective thickness of composite glass, including polymeric laminated glass (with polyvinyl butyral (PVB) and SentryGlas^® (SGP) interlayers) and vacuum glazing. This method employs comparative four-point bending tests, defining effective thickness by equating the bending stress of a composite specimen to that of a reference monolithic glass specimen under identical loading and boundary conditions. Specimens with varying configurations (glass thicknesses of 5 mm, 6 mm and 8 mm) were tested using non-destructive four-point bending tests under a multi-stage loading protocol (100 N–1000 N). Strain rosettes measured maximum strains at each loading stage to calculate bending stress. Analysis of the bending stress state revealed that vacuum glazing and SGP laminated glass exhibit superior load-bearing capacity compared to PVB laminated glass. The proposed method successfully determined the effective thickness for both laminated glass and vacuum glazing. Furthermore, results demonstrate that employing a 12 mm monolithic reference glass provides the highest accuracy for effective thickness determination. Theoretical bending stress calculations using the effective thickness derived from the 12 mm reference glass showed less than 10% deviation from experimental values. Conversely, compared to established standards and empirical formulas, the proposed method offers superior accuracy, particularly for vacuum glazing. Additionally, the mechanical properties of the viscoelastic interlayers (PVB and SGP) were investigated through static tensile tests and dynamic thermomechanical analysis (DMA). Distinct tensile behaviors and differing time-dependent shear transfer capacities between the two interlayer materials are found out. Key factors influencing the reliability of the method are also discussed and analyzed. This study provides a universally practical and applicable solution for accurate and effective thickness estimation in composite glass design. Full article

Background/Objectives: Osteonecrosis of the femoral head (ONFH) is a common condition in hip surgery, which is characterized by the death of bone cells due to disruption of the blood supply and ultimately irreversible destruction of the hip joint. As a result of the COVID-19 pandemic, a significant increase in the incidence of ONFH has been identified. To better understand the pathogenesis of ONFH in the context of COVID-19, our research aimed to determine pathomorphological changes in articular tissues specific to post-COVID-19 ONFH. Methods: Using morphological, morphometric, and statistical methods, the femoral heads after hip arthroplasty were retrospectively studied in patients with post-COVID-19 ONFH (n = 41) compared to a non-COVID-19 group of patients (n = 47). Results: Our results revealed that the key morphofunctional biomarkers of post-COVID-19 ONFH were clusters of mast cells, extensive areas of fibrosis, numerous arterial and venous thrombi, and giant cell granulomas. The potential relationship of those morphological features with the action of the SARS-CoV-2 coronavirus was discussed. Conclusions: Mast cells have been proposed as the leading players that may trigger the main molecular and cellular mechanisms in the development of post-COVID-19 ONFH and can be considered a diagnostic sign of the disease. Full article

Aflatoxin B₁ (AFB₁) contamination in corn silage poses significant risks to livestock and human health. This study developed a non-destructive detection method for AFB₁ using color-sensitive arrays (CSAs). Twenty self-developed CSAs were employed to react with samples, with reflectance spectra collected using a portable spectrometer. Spectral data were optimized through seven preprocessing methods, including Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), first-order derivative (1st D), second-order derivative (2nd D), wavelet denoising, and their combinations. Key variables were selected using five feature selection algorithms: Competitive Adaptive Reweighted Sampling (CARS), Principal Component Analysis (PCA), Random Forest (RF), Uninformative Variable Elimination (UVE), and eXtreme Gradient Boosting (XGBoost). Five machine learning models were constructed: Light Gradient Boosting Machine (LightGBM), XGBoost, Support Vector Regression (SVR), RF, and K-Nearest Neighbor (KNN). The results demonstrated significant AFB₁-responsive characteristics in three dyes: (2,3,7,8,12,13,17,18-octaethylporphynato)chloromanganese(III) (Mn(OEP)Cl), Bromocresol Green, and Cresol Red. The combined 1st D-PCA-KNN model showed optimal prediction performance, with determination coefficient ($R_p^2$ = 0.87), root mean square error (RMSEP = 0.057), and relative prediction deviation (RPD = 2.773). This method provides an efficient solution for silage AFB₁ monitoring. Full article

Model updating is a challenging task with regard to maintaining the performance of non-destructive detection models while using hyperspectral imaging techniques for detecting the internal quality of fresh fruits like blueberries. Different sample batches and differences in hyperspectral image acquisition environments may lead to a significant decline in the performance of hyperspectral detection models. This study investigated the transferability of a hyperspectral model for the quantitating soluble solid content of blueberries across different batches for two harvest years. Hyperspectral images and SSC values of blueberries were collected from two batches, including 364 samples from 2024 and 175 samples from 2025. The differences between SSC measurements and spectral data across these two batches were analyzed. Based on the sample dataset of the year 2024, a high-performance quantitative model for detecting SSC values was established by combining it with partial least squares regression (PLSR) and competitive adaptive reweighted sampling (CARS). This high-performance model could achieve a high determination coefficient (${\mathrm{R}}_{\mathrm{P}}^2$) of 0.8965 and a low root mean square error of prediction (RMSEP) of 0.3707 °Brix. Using the sample dataset for the year 2025, the hyperspectral model was updated by the semi-supervised parameter-free calibration enhancement (SS-PFCE) algorithm. The updated model performed better than those established using individual datasets from 2024 and 2025, and obtained an ${\mathrm{R}}_{\mathrm{P}}^2$ of 0.8347 and an RMSEP of 0.4930 °Brix. This indicates that the calibration transfer strategy is superior in improving hyperspectral model performance. This study demonstrated that the SS-PFCE algorithm, as a calibration transfer strategy, could effectively improve the transferability of the established model for detecting the SSC of blueberries across different sample batches. Full article

Vibrational spectroscopy, including Raman and near-infrared techniques, enables the non-destructive evaluation of starch gelatinization, head rice yield, and aroma-active volatile compounds in parboiled rice subjected to varying soaking and drying conditions. Raman and NIR spectra were collected for rice samples processed under different conditions and integrated with reference analyses to develop and validate partial least squares regression and artificial neural network models. The optimized PLSR model demonstrated strong predictive performance, with R² values of 0.9406 and 0.9365 for SG and HRY, respectively, and residual predictive deviations of 3.98 and 3.75 using Raman effective wavelengths. ANN models reached R² values of 0.97 for both SG and HRY, with RPDs exceeding 4.2 using NIR effective wavelengths. In the aroma compound analysis, p-Cymene exhibited the highest predictive accuracy, with R² values of 0.9916 for calibration, and 0.9814 for cross-validation. Other volatiles, such as 1-Octen-3-ol, nonanal, benzaldehyde, and limonene, demonstrated high predictive reliability (R² ≥ 0.93; RPD > 3.0). Conversely, farnesene, menthol, and menthone showed poor predictability (R² < 0.15; RPD < 0.4). Principal component analysis revealed that the first principal component explained 90% of the total variance in the Raman dataset and 71% in the NIR dataset. Hotelling’s T² analysis identifies influential outliers and enhances model robustness. Optimal processing conditions for achieving maximum HRY and SG values were determined at 65 °C soaking for 180 min, followed by drying at 70 °C. This study underscores the potential of integrating vibrational spectroscopy with machine learning techniques and targeted wavelength selection for the high-throughput, accurate, and scalable quality evaluation of parboiled rice. Full article

Potassium (K), a critical macronutrient for the growth and development of Korla fragrant pear (Pyrus sinkiangensis Yu), plays a pivotal regulatory role in sugar-acid metabolism. Furthermore, K exhibits a highly specific response in near-infrared (NIR) spectroscopy compared to elements such as nitrogen (N) and phosphorus (P). Given its fundamental impact on fruit quality parameters, the development of rapid and non-destructive techniques for K determination is of significant importance for precision fertilization management. By measuring leaf potassium content at the fruit setting, expansion, and maturity stages (decreasing from 1.60% at fruit setting to 1.14% at maturity), this study reveals its dynamic change pattern and establishes a high-precision prediction model by combining near-infrared spectroscopy (NIRS) with machine learning algorithms. “Near-infrared spectroscopy coupled with machine learning can enable accurate, non-destructive monitoring of potassium dynamics in Korla pear leaves, with prediction accuracy (R²) exceeding 0.86 under field conditions.” We systematically collected a total of 9000 leaf samples from Korla fragrant pear orchards and acquired spectral data using a benchtop near-infrared spectrometer. After preprocessing and feature extraction, we determined the optimal modeling method for prediction accuracy through comparative analysis of multiple models. Multiplicative scatter correction (MSC) and first derivative (FD) are synergistically employed for preprocessing to eliminate scattering interference and enhance the resolution of characteristic peaks. Competitive adaptive reweighted sampling (CARS) is then utilized to screen five potassium-sensitive bands, specifically in the regions of 4003.5–4034.35 nm, 4458.62–4562.75 nm, and 5145.15–5249.29 nm, among others, which are associated with O-H stretching vibration and changes in water status. A comparison between random forest (RF) and BP neural network indicates that the MSC + FD–CARS–BP model exhibits the optimal performance, achieving coefficients of determination (R²) of 0.96% and 0.86% for the training and validation sets, respectively, root mean square errors (RMSE) of 0.098% and 0.103%, a residual predictive deviation (RPD) greater than 3, and a ratio of performance to interquartile range (RPIQ) of 4.22. Parameter optimization revealed that the BPNN model achieved optimal stability with 10 neurons in the hidden layer. The model facilitates rapid and non-destructive detection of leaf potassium content throughout the entire growth period of Korla fragrant pears, supporting precision fertilization in orchards. Moreover, it elucidates the physiological mechanism by which potassium influences spectral response through the regulation of water metabolism. Full article

Power cable dielectrics must be tested to ascertain their insulation integrity after their design and manufacture. In Southern Africa, power cables must undergo testing in accordance with the South African National Standard (SANS) 1339. The SANS 1339 provides a destructive diagnostic method to evaluate voltage breakdown strength and water tree growth. The shortfall is that there is no provision for the non-destructive determination of the residual strength and assessment of the condition of the power cables. It is possible that non-destructive tests are available. However, a question arises as to how they compare in effectiveness, which is the intention of this study. Accelerated aging at 500 Hz was conducted on the water-retardant cross-linked polyethene (TR-XLPE) power cable sample specimens, each 10 m long, according to SANS 1339. Non-destructive diagnostic tests (Tan δ, IRC, and RVM) were conducted on accelerated-aged and unaged cable samples. The comparative results of the accelerated-aged and unaged XPLE power cable samples, when applying non-destructive diagnostic techniques, show consistency and reveal the extent of degradation in the tested cable samples. This study demonstrates that non-destructive diagnostic methods can be used to assess the extent of XLPE power cable insulation aging. 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

This study is based on the laser-induced thermal-crack propagation (LITP) technology, focusing on the issues of deviation and thermal damage during the transverse crack propagation process, with the aim of achieving high-purity, non-destructive, and high-precision cutting of glass. A 50 W, 1064 nm fiber laser is used for S-pattern scanning cutting of soda–lime glass. A moving heat source model is established and analyzed via MATLAB R2022a numerical simulation. Combined with the ABAQUS 2019 software, the relationships among temperature field, stress field, crack propagation, and deviation during laser-induced thermal crack cutting are deeply explored. Meanwhile, laser thermal fracture experiments are also carried out. A confocal microscope detects glass surface morphology, cross-sectional roughness and hardness under different heat flux densities (HFLs), determining the heat flux density threshold affecting the glass surface quality. Through a comprehensive study of theory, simulation, and experiments, it is found that with an increase in the HFL value of the material, the laser-induced thermal crack propagation can be divided into four stages. When the heat flux density value is in the range of 47.2 to 472 W/m², the glass substrate has good cross-sectional characteristics. There is no ablation phenomenon, and the surface roughness of the cross-section is lower than 0.15 mm. The hardness decreases by 9.19% compared with the reference value. Full article

Acoustic tomography is a non-destructive technique used in the internal assessment of standing trees. Various researchers have focused on developing analytical tools using this technique, demonstrating that they can detect internal biodeterioration in cross-sections with good accuracy. This study evaluates the use of two ultrasonic wave devices with different frequencies (USLab and Sylvatest Duo) and a stress wave device (Microsecond Timer) to generate acoustic tomography using ImageWood VC1 software. The tests were carried out on 12 cross-sections of urban trees in the city of Madrid of the species Robinia pseudoacacia L., Platanus × hybrida Brot., Ulmus pumila L., and Populus alba L. Velocity measurements were made, forming a diffraction mesh in both standing trees and logs after cutting them down. An inspection was carried out with a perforation resistance drill (IML RESI F-400S) in the radial direction in each section, which allowed for more precise identification of defects and differentiating between holes and cracks. The various defects were determined with greater accuracy in the tomographic images taken with the higher-frequency equipment (45 kHz), and the combination of ultrasonic tomography and the use of the inspection drill can provide a more accurate representation of the defects. Full article

It is proposed to reduce the statistical uncertainty to make informed decisions in pavement construction, using a non-destructive method to determine the density (p) of asphalt mixtures, a decisive parameter to know the quality of the material studied and the content of voids (air voids), contrasting the results with destructive and physical tests to specimens extracted at the test site. This was carried out in the field with the EM density gauge (electromagnetic), on a 71.2 km long stretch of road. The results of the non-destructive tests were compared with the AASHTO standards. The study was focused on a representative sample of 25.9% of the total population, obtained using intentional stratified statistical sampling; the standard deviation was taken as the decisive value of dispersion in the determination of the p-density of the mixtures. The AASHTO T343 standard establishes that the permissible standard deviation for asphalt mixtures should be 0.050 g/cm³. Supplementary statistical analysis shows that the measurement error of the EM densitometer and the core-sampling method is ±1.8%, and the correlation coefficient within the 95% confidence interval reaches 0.91. The results of the analysis show a convincing trend towards the implementation of non-destructive methods, such as EM density gauge, to guarantee the determination of the quality of asphalt mixtures in the field, reducing the time required to determine the quality of the asphalt mixes. The results of the analysis show a convincing trend towards the implementation of non-destructive methods, such as EM density gauge, to ensure the determination of the quality of asphalt mixtures in the field, reducing the time required to determine the quality of asphalt mixtures. Full article