Journal of Nondestructive Evaluation最新文献

The aging of transportation infrastructure has highlighted the need for reliable bridge deck assessment methods. Among various non-destructive technologies, ground penetrating radar (GPR) stands out for its ability to evaluate both concrete sections and reinforcement conditions without structural interference. While GPR offers significant advantages over traditional inspection methods such as chain dragging, data interpretation remains challenging due to signal complexity and environmental factors. Recent advances in signal processing, machine learning, and artificial intelligence (AI) have opened new possibilities for enhancing GPR data interpretation and automation. This review paper synthesizes and critically examines recent developments in GPR data analysis for bridge deck evaluation, from conventional signal processing to emerging computational approaches. Advances in five key areas are explored: basic GPR processing algorithms, traditional concrete evaluation methods, machine learning applications in concrete assessment, conventional reinforcement analysis techniques, and artificial intelligence-based reinforcement evaluation. Traditional methods and emerging AI approaches each offer distinct capabilities, with traditional techniques providing the foundation for targeted assessments, while machine learning and deep learning techniques introduce new potential for automated analysis. Studies across various test beds reveal that performance metrics are strongly influenced by testing conditions, data acquisition parameters, and structural characteristics. This diversity in reported outcomes highlights both the significant progress made in GPR data analysis and the continuing challenges in achieving reliable results across varied field conditions.

Accurate semantic segmentation of point cloud data is vital for safety monitoring and intelligent management in tunnel construction. However, challenges such as cluttered environments, occlusions, and the lack of annotated domain-specific datasets hinder effective application of deep learning techniques. To address these issues, this study presents TCS-Net, a novel point cloud segmentation network tailored for under-construction tunnels. A large-scale annotated dataset, named 3D Tunnel, was constructed using handheld laser scanning and contains over 60 million points across eight structural categories, filling a critical data gap in the field. TCS-Net introduces a multi-module fusion framework that combines spatial attention mechanisms, an inverted residual MLP (InvResMLP) for enriched feature representation, and a Kd-tree–based Gaussian upsampling with channel attention for enhanced feature propagation. An optimized training strategy incorporating AdamW, cosine decay, and label smoothing further improves learning robustness. Experimental results on the 3D Tunnel dataset demonstrate that TCS-Net achieves superior segmentation performance, with 94.38% mean IoU and 98.23% overall accuracy, validating its effectiveness and practical potential in tunnel construction scenarios.

This article presents a high-speed z-shaped laser point scanning thermography detection method and its corresponding experimental system. The system is designed for the rapid inspection of specimens. By analyzing the thermal response of the z-shaped scan thermography, a thermal response signal reconstruction method based on the restored pseudo heat flux (RPHF) theory, which is autocorrelated RPHF (RPHF-autocorr), is proposed, and the principle of the process is discussed. Additionally, the proposed method corrects the speckle and time offsets in the point laser scanning infrared thermal imaging. Experiments were conducted on carbon fiber reinforced polymer (CFRP) composites, where the raw thermal images are obtained and processed using the proposed algorithm. The signal-to-noise ratio (SNR) of defects is calculated and used to evaluate the defect detectability. The results are compared to the pseudo-static matrix reconstruction time truncation (PSMRT) and PSMRT pulse phase thermography (PSMRT-PPT). The experimental results show that the RPHF-autocorr algorithm provides a relatively high SNR. In summary, the RPHF-autocorr algorithm significantly improves the SNR of defects, increases defect detection sensitivity, and reduces the impact of lateral thermal diffusion caused by the moving laser spot.

The analysis of secondary quantitative data extracted from high-resolution synchrotron X-ray computed tomography scans represents a significant challenge for users. While a number of methods have been introduced for processing large three-dimensional images in order to generate secondary data, there are only a few techniques available for simple and intuitive visualization of such data in their entirety. This work employs the AccuStripes visualization technique for that purpose, which enables the visual analysis of secondary data represented by an ensemble of univariate distributions. It supports different schemes for adaptive histogram binnings in combination with several ways of rendering aggregated data and it allows the interactive selection of optimal visual representations depending on the data and the use case. We demonstrate the usability of AccuStripes on two high-resolution synchrotron scans of particle-reinforced metal matrix composite samples, each containing millions of particles. Through AccuStripes, detailed insights are facilitated into distributions of derived particle characteristics of the entire sample. Furthermore, research questions such as how the overall shape of the particles is or how homogeneously they are distributed across the sample can be answered.

Wind turbine blades (WTBs) have increased in size and complexity, resulting in higher operational demands and maintenance costs. Damage to these blades can significantly reduce turbine performance, lifespan, and power generation, while increasing safety risks. Effective structural health monitoring (SHM) is therefore essential for early damage detection and failure prevention. This paper presents a comprehensive review of various SHM techniques for WTBs, categorizing each technique into sensing methods (data acquisition) and analysis methods (data processing and interpretation). The review also addresses the causes and types of blade damage, severity ratings along with corresponding maintenance actions, and fatigue-induced damage progression. Advanced approaches, including machine learning, signal processing, hybrid methods, and emerging techniques such as piezo-based active sensing, electromechanical impedance, and Lamb wave tomography, are also explored for their potential to enhance SHM capabilities. Additionally, commercially available SHM systems and inspection platforms, such as unmanned aerial vehicles, are reviewed to highlight practical applicability. The review covers strain-based methods, acoustic emission, vibration analysis, thermography, ultrasonic testing, radiography, machine vision, and electromagnetic techniques, highlighting their advantages, limitations, and future research directions for improving SHM for WTBs.

A method is proposed for increasing the signal-to-noise ratio SNR of surface eddy current magnetic permeability meters with orthogonal rectangular coils for a moving strip of ferromagnetic rolled metal. As a result of robust parameter design of the probes, their optimal structures are determined using the design of experiments theory. Specific examples demonstrate the effectiveness of the method, which, in addition to increasing the SNR, also simultaneously reduces variations in the output signal caused by interfering factors. To create a Taguchi design of experiments, a magnetodynamic model of the probe over a moving test object was used along with orthogonal arrays. Numerical computer simulations confirm the reliability of the identified optimal structures of eddy current probes. Using variance ANOVA analysis, the ranks of influence of design and operating parameters on the SNR of probes were established. Based on this, technical requirements for the accuracy of manufacturing their structures and conditions for maintaining the stability of the probe’s operating modes have been formulated.

Pork is an important meat product for the European Union, which exported over 4.2 million tons in 2023, valued at €8.1 billion. Automating the labor-intensive deboning process is of significant interest, particularly through the development of advanced inline inspection systems capable of analyzing pork shoulder bone structures. While computed tomography (CT) systems provide high-contrast 3D reconstructions, their large size and high-cost present substantial barriers to adoption in industrial meat processing. This study addresses these challenges by introducing a novel approach that uses a single X-ray projection in combination with deep neural networks to predict the 3D segmentation map of pork shoulder bone structures using conventional reconstruction algorithms. To this end, U-Net neural network variants were trained on high-resolution CT scans of 90 pork shoulders. These scans were augmented with synthetic data to simulate different orientations on a conveyor belt, ensuring the model’s robustness. The minimum number of X-ray projections needed for accurate reconstruction was determined based on simulations, and 60 evenly spaced projections between 0° and 180° were found optimal. The Feldkamp-Davis-Kress (FDK) algorithm was chosen for its efficiency and cost-effectiveness in inline processing. The model achieved a Dice score of 0.94 and an SSIM of 0.96 on test data, demonstrating its ability to predict 59 missing projections and reconstruct the 3D bone structure accurately. The method that is proposed in this paper has the potential to advance meat processing by enhancing deboning precision, reducing waste, and streamlining operations. Our best model achieved an average dice score of 0.94 ± 0.03 and a maximum voxel-wise error of 16 mm on our test set, indicating high segmentation accuracy and spatial consistency in bone structure reconstruction. While the results are promising, the current evaluation is based on synthetic X-ray projections. Future work will focus on validating the method with real inline acquisitions and assessing its impact on cutting precision and waste reduction in robotic deboning.

Accurate identification of structural states during the hoisting of high-speed railway (HSR) precast box girders is essential for ensuring construction safety. However, efficient identification of girder status during hoisting remains a major challenge due to the complexity of mechanical responses at lifting points. To explore the feasibility and effectiveness of acoustic emission (AE) monitoring in identifying hoisting states, this study proposes a status recognition method that integrates AE sensing at lifting points with an optimized classification model. Specifically, (1) AE signals are collected during various hoisting scenarios of standard 32.6 m HSR box girders to capture characteristic changes in signal features such as amplitude, energy, ring counts, etc. (2) Based on a comprehensive feature set extracted from the AE signals, a light gradient boosting machine (LGBM) classification model optimized via Bayesian algorithm is developed for hoisting state recognition. Experimental tests conducted in a prefabrication yard demonstrate that the proposed method effectively distinguishes different hoisting conditions, particularly capturing potential anomalies caused by lifting asynchrony or stress concentration. The results validate the applicability of AE technology for non-invasive, efficient status identification during girder hoisting, providing a technical foundation for the intelligent monitoring of construction safety.

Open-loop permanent magnet magnetizers are widely employed in magnetic flux leakage (MFL) testing of steel wire ropes due to their structural simplicity and operational convenience. However, the underlying magnetization mechanism remains inadequately understood, and systematic investigations into their optimal structural configurations are still lacking. In this study, a combined approach of finite element simulation and experimental validation is utilized to systematically examine the influence of axial length on the magnetization performance of ring-type permanent magnet magnetizers for steel wire ropes modeled as equivalent steel tubes. The results reveal a nonlinear relationship between axial length and the peak axial magnetic flux density in air: the intensity initially increases and then decreases with length. In contrast, both the magnetic field uniformity and the effective magnetization region improve monotonically with increasing length. These two mechanisms jointly modulate the internal magnetization state of the tube, giving rise to an optimal axial length. Further analysis confirms that for a steel wire rope with a diameter of 50 mm (or its equivalent tube), the optimal axial length range of the open-ring magnetizer lies between 95 mm and 110 mm, within which an optimal balance between field strength.

Real-time monitoring of bridge deterioration remains a major challenge in structural health monitoring (SHM), as traditional inspection methods lack sensitivity to early-stage damage and cannot provide real-time evaluation. This study aims to develop a practical approach for damage stage assessment of prestressed hollow-slab bridges using acoustic emission (AE) parameters and a lightweight machine learning model. First, scaled model tests were conducted to collect AE signals under different loading stages, and parameters such as amplitude, energy, duration, ring-down count, and impact velocity were extracted. Second, correlation analyses (Pearson, Spearman, Kendall) were performed to identify the most representative parameters for structural degradation. Third, a K-nearest neighbors (KNN) model was then constructed to classify bridge damage stages in real-time. Finally, comparative analysis with decision tree, support vector machine (SVM), one-dimensional convolutional neural network (1DCNN), and long short-term memory (LSTM) models was conducted. Impact Velocity was determined to have the strongest correlation with damage stages and the KNN model achieved competitive accuracy (0.9103) with superior computational efficiency (1.30 × 10⁻⁶ s per sample), offered the best computational efficiency, making it highly suitable for real-time applications. This research establishes an efficient, data-driven framework for real-time bridge health monitoring, demonstrating the practical viability of combining AE technology with lightweight machine learning for structural damage assessment. The methodology shows particular promise for implementation in real-time monitoring systems for prestressed concrete structures.