Pub Date : 2026-01-27DOI: 10.1016/j.ndteint.2026.103660
Jun Hou, Hu Sun, Xinlin Qing
Remote field eddy current (RFEC) testing offers deep-penetration capability for subsurface inspection, but its application to confined multi-layer geometries such as bolted joints remains unexplored. This study proposes an embedded RFEC method that combines flexible eddy current sensor integration with analytical and finite element modeling to elucidate the formation mechanism of the remote field within bolted joints. The effects of excitation frequency, material properties, and bolt geometry on RFEC coupling are systematically analyzed. Experimental validation on aluminum bolted joints demonstrates that under 3 kHz excitation, crack depths up to 10 mm can be monitored by the sensor, corresponding to amplitude and phase changes of 53 μV and 0.55°, respectively. The location, length, and depth of cracks can be monitored based on sensor signal characteristics. The research results validate the feasibility and high sensitivity of the embedded RFEC method, extending the application of RFEC to complex structural health monitoring scenarios.
{"title":"Remote field eddy current monitoring of hole-edge cracks in bolted joints: Theoretical modeling and experimental validation","authors":"Jun Hou, Hu Sun, Xinlin Qing","doi":"10.1016/j.ndteint.2026.103660","DOIUrl":"10.1016/j.ndteint.2026.103660","url":null,"abstract":"<div><div>Remote field eddy current (RFEC) testing offers deep-penetration capability for subsurface inspection, but its application to confined multi-layer geometries such as bolted joints remains unexplored. This study proposes an embedded RFEC method that combines flexible eddy current sensor integration with analytical and finite element modeling to elucidate the formation mechanism of the remote field within bolted joints. The effects of excitation frequency, material properties, and bolt geometry on RFEC coupling are systematically analyzed. Experimental validation on aluminum bolted joints demonstrates that under 3 kHz excitation, crack depths up to 10 mm can be monitored by the sensor, corresponding to amplitude and phase changes of 53 μV and 0.55°, respectively. The location, length, and depth of cracks can be monitored based on sensor signal characteristics. The research results validate the feasibility and high sensitivity of the embedded RFEC method, extending the application of RFEC to complex structural health monitoring scenarios.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103660"},"PeriodicalIF":4.5,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-27DOI: 10.1016/j.ndteint.2026.103662
Jelena Bijeljić , Emina Petrović , Ernst Niederleithinger
Despite the growing interest in applying artificial intelligence (AI) in civil engineering, its use for evaluating concrete properties remains relatively underexplored. In particular, the assessment of air permeability, a key parameter for concrete durability and long-term performance, has not been extensively addressed using AI-based approaches.
Traditional methods, such as the Torrent test, provide reliable measurements but are time-consuming, labor-intensive, and require specialized equipment. In this study, an image-based deep learning framework was employed, where surface images of concrete specimens served as input data, and the air permeability coefficient kT, measured using the Torrent tester, was used as ground truth. Concrete mixtures were categorized into two classes: “Poor” (low quality) and “Very Poor” (very low quality). Nine batches of cement-based concrete mixtures were prepared, varying in maximum aggregate size and the dosage of air-entraining agents (LP). Deep learning models were developed to link visual surface features with the corresponding air permeability classes. Model performance was evaluated using a combination of statistical measures, including accuracy, precision, recall, F1-score, confusion matrices, ROC-AUC, and PR-AUC, computed across all folds of a 10-fold cross-validation procedure. One-way ANOVA and Tukey's HSD post-hoc test were applied to verify the statistical significance of performance differences. For models achieving the best performance, Gradient-weighted Class Activation Mapping (Grad-CAM) was used to highlight image regions that most strongly influenced the CNN predictions, providing visual insight into the learned feature representations. The results demonstrated that the ResNet50 architecture achieved the most reliable classification performance, highlighting the potential of image-based AI approaches for non-destructive, automated, and field-applicable assessment of concrete air permeability.
尽管人们对人工智能（AI）在土木工程中的应用越来越感兴趣，但它在评估混凝土性能方面的应用仍然相对不足。特别是，空气渗透性的评估，混凝土耐久性和长期性能的关键参数，尚未广泛解决使用基于人工智能的方法。传统的方法，如Torrent测试，提供了可靠的测量结果，但耗时，劳动密集，并且需要专门的设备。本研究采用基于图像的深度学习框架，以混凝土试件表面图像作为输入数据，以Torrent测试仪测量的空气渗透系数kT作为地面真值。混凝土混合物被分为两类：“差”（低质量）和“非常差”（非常低质量）。制备了9批水泥基混凝土混合料，其最大骨料粒径和引气剂（LP）的掺量不同。开发了深度学习模型，将视觉表面特征与相应的透气性类别联系起来。模型的性能使用统计测量的组合进行评估，包括准确性、精密度、召回率、f1得分、混淆矩阵、ROC-AUC和PR-AUC，在10次交叉验证程序的所有折叠中计算。采用单因素方差分析和Tukey’s HSD事后检验验证成绩差异的统计学意义。对于获得最佳性能的模型，使用梯度加权类激活映射（Gradient-weighted Class Activation Mapping, Grad-CAM）来突出显示对CNN预测影响最大的图像区域，提供对学习到的特征表示的视觉洞察。结果表明，ResNet50架构实现了最可靠的分类性能，突出了基于图像的人工智能方法在非破坏性、自动化和现场适用的混凝土透气性评估方面的潜力。
{"title":"Application of AI-based techniques for concrete air permeability classification","authors":"Jelena Bijeljić , Emina Petrović , Ernst Niederleithinger","doi":"10.1016/j.ndteint.2026.103662","DOIUrl":"10.1016/j.ndteint.2026.103662","url":null,"abstract":"<div><div>Despite the growing interest in applying artificial intelligence (AI) in civil engineering, its use for evaluating concrete properties remains relatively underexplored. In particular, the assessment of air permeability, a key parameter for concrete durability and long-term performance, has not been extensively addressed using AI-based approaches.</div><div>Traditional methods, such as the Torrent test, provide reliable measurements but are time-consuming, labor-intensive, and require specialized equipment. In this study, an image-based deep learning framework was employed, where surface images of concrete specimens served as input data, and the air permeability coefficient <em>kT</em>, measured using the Torrent tester, was used as ground truth. Concrete mixtures were categorized into two classes: “Poor” (low quality) and “Very Poor” (very low quality). Nine batches of cement-based concrete mixtures were prepared, varying in maximum aggregate size and the dosage of air-entraining agents (LP). Deep learning models were developed to link visual surface features with the corresponding air permeability classes. Model performance was evaluated using a combination of statistical measures, including accuracy, precision, recall, F1-score, confusion matrices, ROC-AUC, and PR-AUC, computed across all folds of a 10-fold cross-validation procedure. One-way ANOVA and Tukey's HSD post-hoc test were applied to verify the statistical significance of performance differences. For models achieving the best performance, Gradient-weighted Class Activation Mapping (Grad-CAM) was used to highlight image regions that most strongly influenced the CNN predictions, providing visual insight into the learned feature representations. The results demonstrated that the ResNet50 architecture achieved the most reliable classification performance, highlighting the potential of image-based AI approaches for non-destructive, automated, and field-applicable assessment of concrete air permeability.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103662"},"PeriodicalIF":4.5,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To improve the accuracy of damage localization in parallel wire strands (PWS) used in cable-stayed bridges and optimize the arrangement of acoustic emission (AE) sensors, an analytical model describing the attenuation of AE signal amplitude across the PWS cross-section was developed. Attenuation tests were then conducted using pencil lead break (PLB) and center punch impacts as simulated damage sources, followed by a sensitivity analysis. The comparison between test results and analytical solutions shows that the analytical model is more suitable for low-frequency signal analysis, with deviations increasing as the signal frequency rises. The analytical model and test result both demonstrate that high-frequency components of AE signals attenuate more rapidly within the PWS cross-section, and sensors with lower resonant frequencies yield superior performance. As the AE signal frequency increases, so does the energy dissipation during propagation. When the frequency rises from 5 kHz to 100 kHz, the attenuation coefficient and acoustic impedance ratio increase by factors of 4.17 and 4.31, respectively. For damage monitoring of bridge PWS, both the resonant frequency of the sensor and the peak signal energy should be considered, with priority given to the resonant frequency.
{"title":"Propagation characteristics of acoustic emission signals across the cross-section of parallel wire strands","authors":"Zhitao Sun , Dongming Feng , Yixuan Zhao , Futang Wei","doi":"10.1016/j.ndteint.2026.103659","DOIUrl":"10.1016/j.ndteint.2026.103659","url":null,"abstract":"<div><div>To improve the accuracy of damage localization in parallel wire strands (PWS) used in cable-stayed bridges and optimize the arrangement of acoustic emission (AE) sensors, an analytical model describing the attenuation of AE signal amplitude across the PWS cross-section was developed. Attenuation tests were then conducted using pencil lead break (PLB) and center punch impacts as simulated damage sources, followed by a sensitivity analysis. The comparison between test results and analytical solutions shows that the analytical model is more suitable for low-frequency signal analysis, with deviations increasing as the signal frequency rises. The analytical model and test result both demonstrate that high-frequency components of AE signals attenuate more rapidly within the PWS cross-section, and sensors with lower resonant frequencies yield superior performance. As the AE signal frequency increases, so does the energy dissipation during propagation. When the frequency rises from 5 kHz to 100 kHz, the attenuation coefficient and acoustic impedance ratio increase by factors of 4.17 and 4.31, respectively. For damage monitoring of bridge PWS, both the resonant frequency of the sensor and the peak signal energy should be considered, with priority given to the resonant frequency.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103659"},"PeriodicalIF":4.5,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Intelligent weld defect assessment is a growing research focus. However, existing methods overlook non-destructive testing (NDT) radiographic interpretation standards and defect formation mechanisms, leading to missed or false detections in low-contrast or blurred-boundary regions, and misclassification of defect types. This study proposes an artificial intelligence (AI)-based method for detecting pipeline girth weld defects, integrating NDT domain knowledge with data and learning algorithms. First, inspired by how human inspectors visually scan long-scale images locally and sequentially, a semi-overlapping sliding window strategy is designed to preprocess full-length images while preserving original information. Second, inspired by the dynamic film evaluation process, a defect detection model based on the You Only Look Once (YOLO)v8 architecture is proposed, incorporating multi-image decomposition, keyframe selection, and multi-image feature fusion strategies. Finally, by analyzing the formation mechanisms of weld defects, a classification rule set covering eight typical defect types is established to support final defect-type determination. Experimental results demonstrate that the proposed “NDT domain knowledge + data + AI” paradigm outperforms state-of-the-art approaches, particularly in detecting concave, porosity, and slag defects. In addition, it achieves 100 % recall in burn-through and crack detection. This study provides new insights and technical support for the future development of intelligent weld defect recognition systems.
焊缝缺陷智能评估是一个日益发展的研究热点。然而，现有的方法忽略了无损检测（NDT）射线成像解释标准和缺陷形成机制，导致在低对比度或模糊边界区域漏检或误检，以及缺陷类型的错误分类。本研究提出了一种基于人工智能（AI）的管道环焊缝缺陷检测方法，将无损检测领域知识与数据和学习算法相结合。首先，受人类检查员在局部和顺序上视觉扫描长尺度图像的启发，设计了半重叠滑动窗口策略，在保留原始信息的情况下对全长图像进行预处理。其次，受动态胶片评价过程的启发，提出了一种基于You Only Look Once (YOLO)v8架构的缺陷检测模型，该模型融合了多图像分解、关键帧选择和多图像特征融合策略。最后，通过分析焊接缺陷的形成机理，建立了涵盖八种典型缺陷类型的分类规则集，以支持最终缺陷类型的确定。实验结果表明，提出的“无损检测领域知识+数据+人工智能”模式优于当前的方法，特别是在检测凹、孔隙和渣缺陷方面。此外，它在烧透和裂纹检测中实现100%召回。该研究为未来智能焊缝缺陷识别系统的发展提供了新的见解和技术支持。
{"title":"Intelligent detection of pipeline girth weld defects: a non-destructive testing domain knowledge-integrated approach","authors":"Yong Zhang , Hongquan Jiang , Huyue Cheng , Tianjun Liu , Yuhang Qiu , Deyan Yang , Peng Liu , Jianmin Gao , Zelin Zhi , Deqiang Jing , Xiaoming Zhang","doi":"10.1016/j.ndteint.2026.103653","DOIUrl":"10.1016/j.ndteint.2026.103653","url":null,"abstract":"<div><div>Intelligent weld defect assessment is a growing research focus. However, existing methods overlook non-destructive testing (NDT) radiographic interpretation standards and defect formation mechanisms, leading to missed or false detections in low-contrast or blurred-boundary regions, and misclassification of defect types. This study proposes an artificial intelligence (AI)-based method for detecting pipeline girth weld defects, integrating NDT domain knowledge with data and learning algorithms. First, inspired by how human inspectors visually scan long-scale images locally and sequentially, a semi-overlapping sliding window strategy is designed to preprocess full-length images while preserving original information. Second, inspired by the dynamic film evaluation process, a defect detection model based on the You Only Look Once <strong>(</strong>YOLO)v8 architecture is proposed, incorporating multi-image decomposition, keyframe selection, and multi-image feature fusion strategies. Finally, by analyzing the formation mechanisms of weld defects, a classification rule set covering eight typical defect types is established to support final defect-type determination. Experimental results demonstrate that the proposed “NDT domain knowledge + data + AI” paradigm outperforms state-of-the-art approaches, particularly in detecting concave, porosity, and slag defects. In addition, it achieves 100 % recall in burn-through and crack detection. This study provides new insights and technical support for the future development of intelligent weld defect recognition systems.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103653"},"PeriodicalIF":4.5,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-23DOI: 10.1016/j.ndteint.2026.103656
Jinbo Li , Nan Zhang , Yuling Zhang
Metal magnetic memory testing (MMMT) has demonstrated considerable potential for the identification and quantitative evaluation of hidden fatigue damage; however, the applicability of current damage evaluation indicators in actual structural inspections remains insufficiently explored. In this study, this issue was examined by designing two types of plate specimens to model the fatigue damage characteristics of orthotropic steel bridge decks. Prefabricated gaps were incorporated to simulate hidden fatigue damage in actual components, and the initial magnetic fields of the specimens were retained. The specimens were subjected to tensile‒tensile fatigue testing, and their surface magnetic fields were monitored online via a three-dimensional probe along predefined scanning paths. Digital image correlation was concurrently utilized on the opposite side of the specimens to verify the capability of the MMMT for fatigue damage detection and to evaluate the reliability of the fatigue life predictions. Analysis of the measured data revealed the limitations within the existing damage evaluation indicators, and new indicators of Mc, Div, and Curl were proposed. To minimize missed and false detections in the MMMT, a joint analysis of local contour maps for these indicators was conducted. By extracting the first-order longitudinal difference characteristic values of the proposed indicators and applying Bayes' theorem, a characteristic value database was established to assess the fatigue life of the specimens. The field detection from three fatigue designs in the orthotropic steel bridge deck of an in-service cable-stayed bridge indicated that the proposed MMMT-based scheme is highly efficacious for detecting the fatigue damage in the steel structures.
{"title":"Fatigue damage detection and assessment of standard plate specimens via metal magnetic memory testing","authors":"Jinbo Li , Nan Zhang , Yuling Zhang","doi":"10.1016/j.ndteint.2026.103656","DOIUrl":"10.1016/j.ndteint.2026.103656","url":null,"abstract":"<div><div>Metal magnetic memory testing (MMMT) has demonstrated considerable potential for the identification and quantitative evaluation of hidden fatigue damage; however, the applicability of current damage evaluation indicators in actual structural inspections remains insufficiently explored. In this study, this issue was examined by designing two types of plate specimens to model the fatigue damage characteristics of orthotropic steel bridge decks. Prefabricated gaps were incorporated to simulate hidden fatigue damage in actual components, and the initial magnetic fields of the specimens were retained. The specimens were subjected to tensile‒tensile fatigue testing, and their surface magnetic fields were monitored online via a three-dimensional probe along predefined scanning paths. Digital image correlation was concurrently utilized on the opposite side of the specimens to verify the capability of the MMMT for fatigue damage detection and to evaluate the reliability of the fatigue life predictions. Analysis of the measured data revealed the limitations within the existing damage evaluation indicators, and new indicators of <em>Mc</em>, <em>Div</em>, and <em>Curl</em> were proposed. To minimize missed and false detections in the MMMT, a joint analysis of local contour maps for these indicators was conducted. By extracting the first-order longitudinal difference characteristic values of the proposed indicators and applying Bayes' theorem, a characteristic value database was established to assess the fatigue life of the specimens. The field detection from three fatigue designs in the orthotropic steel bridge deck of an in-service cable-stayed bridge indicated that the proposed MMMT-based scheme is highly efficacious for detecting the fatigue damage in the steel structures.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103656"},"PeriodicalIF":4.5,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-22DOI: 10.1016/j.ndteint.2026.103645
Min Zhai , Haoyue Pan , Bin Xiao , Haolian Shi , Zhang Qu , Wenlong He , Cong Zhai , Yi Tang
Woven Glass Fiber Reinforced Polymer (GFRP) composites were studied using terahertz time-of-flight tomography to characterize failure modes in GFRP composite in a nondestructive and contactless fashion during in-situ tensile testing. The fracture morphologies of GFRP composite under different applied stresses were discussed by comparing terahertz C-and B-scan images to evaluate the dynamic evolution of tensile-induced microstructure. Our results show that significant THz-detectable damage initiation was observed at stress levels exceeding 60 MPa. In addition, tensile-induced damage can be observed not only on the surface, but also within the inner piles of GFRP composites. Finally, our work verifies the effectiveness of THz-based approach on three-dimensional dynamic monitoring the quality of GFRP composite in service and evaluating the influence of different loading conditions on structural properties and failure pattern of composite materials.
{"title":"Monitoring Tensile-Induced Subsurface Damages of Woven Glass Fiber Reinforced Polymer Using Terahertz Time-of-Flight Tomography","authors":"Min Zhai , Haoyue Pan , Bin Xiao , Haolian Shi , Zhang Qu , Wenlong He , Cong Zhai , Yi Tang","doi":"10.1016/j.ndteint.2026.103645","DOIUrl":"10.1016/j.ndteint.2026.103645","url":null,"abstract":"<div><div>Woven Glass Fiber Reinforced Polymer (GFRP) composites were studied using terahertz time-of-flight tomography to characterize failure modes in GFRP composite in a nondestructive and contactless fashion during <em>in-situ</em> tensile testing. The fracture morphologies of GFRP composite under different applied stresses were discussed by comparing terahertz C-and B-scan images to evaluate the dynamic evolution of tensile-induced microstructure. Our results show that significant THz-detectable damage initiation was observed at stress levels exceeding 60 MPa. In addition, tensile-induced damage can be observed not only on the surface, but also within the inner piles of GFRP composites. Finally, our work verifies the effectiveness of THz-based approach on three-dimensional dynamic monitoring the quality of GFRP composite in service and evaluating the influence of different loading conditions on structural properties and failure pattern of composite materials.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103645"},"PeriodicalIF":4.5,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.ndteint.2026.103655
Lin Wang , Xiucheng Liu , Shurui Zhang , Yangyang Zhang , Zhongqi Xu , Yang Yu
Effective non-destructive evaluation of key microstructural features is essential for quality control and performance prediction of pearlitic steels. This study develops a micromagnetic feature-excitation mapping method to characterize lamellar spacing and cluster size using a single multifunctional sensor. Specimens with controlled microstructures-lamellar spacing and cluster size-were prepared and tested under varied excitation frequencies and amplitudes. Four types of magnetic signals were acquired, and 41 magnetic features were extracted. Analysis of linearity and sensitivity identified optimal feature–excitation combinations for independently evaluating lamellar spacing and cluster size. Two practical strategies are demonstrated: selecting different magnetic feature parameters under fixed excitation or adjusting excitation conditions for a single parameter. The proposed approach enables flexible multi-parameter characterization within one integrated detection system and offers practical guidance for industrial non-destructive testing. Although demonstrated for pearlitic steels, the method can be adapted to other microstructural or mechanical parameters, showing strong potential for broader applications in structural health monitoring and process control.
{"title":"A micromagnetic feature–excitation mapping framework for separate non-destructive characterization of lamellar spacing and cluster size in pearlitic steel","authors":"Lin Wang , Xiucheng Liu , Shurui Zhang , Yangyang Zhang , Zhongqi Xu , Yang Yu","doi":"10.1016/j.ndteint.2026.103655","DOIUrl":"10.1016/j.ndteint.2026.103655","url":null,"abstract":"<div><div>Effective non-destructive evaluation of key microstructural features is essential for quality control and performance prediction of pearlitic steels. This study develops a micromagnetic feature-excitation mapping method to characterize lamellar spacing and cluster size using a single multifunctional sensor. Specimens with controlled microstructures-lamellar spacing and cluster size-were prepared and tested under varied excitation frequencies and amplitudes. Four types of magnetic signals were acquired, and 41 magnetic features were extracted. Analysis of linearity and sensitivity identified optimal feature–excitation combinations for independently evaluating lamellar spacing and cluster size. Two practical strategies are demonstrated: selecting different magnetic feature parameters under fixed excitation or adjusting excitation conditions for a single parameter. The proposed approach enables flexible multi-parameter characterization within one integrated detection system and offers practical guidance for industrial non-destructive testing. Although demonstrated for pearlitic steels, the method can be adapted to other microstructural or mechanical parameters, showing strong potential for broader applications in structural health monitoring and process control.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103655"},"PeriodicalIF":4.5,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-19DOI: 10.1016/j.ndteint.2026.103654
Qing Na , Qiusong Chen , Daolin Wang , Jilong Pan , Yunbo Tao , Aixiang Wu
Accurate and rapid evaluation of the uniaxial compressive strength (UCS) of cemented paste backfill (CPB) is crucial for ensuring safe and efficient underground mining. Traditional UCS tests, however, are contact-based, destructive and time-consuming, which severely limits real-time UCS monitoring and rapid feedback for in-situ backfill quality control. To overcome these limitations, this study innovatively introduces hyperspectral imaging (HSI) technology into the real-time, in-situ UCS monitoring, establishing a non-destructive testing (NDT) method by integrating HSI with artificial intelligence. A total of 120 CPB groups with five mass concentrations (61–73 %) and eight curing ages (3–28 d) were tested to obtain both UCS and corresponding hyperspectral data. The spectral response characteristics of the CPB under varying concentrations were analyzed, revealing that the reflectance gradually increased with concentration, and two distinct absorption peaks were observed near 1400 nm and 1950 nm. Two-dimensional correlation spectroscopy indicated that, under concentration interference, the spectral sensitivity was highest at 1900 nm and lowest at 1600 nm. Subsequently, the effect of various spectral preprocessing techniques and feature extraction algorithms on UCS prediction accuracy was investigated using the PSO-SVM-Bagging algorithm. The results demonstrated that the 2nd D-SG-Nor + UVE model exhibited the best performance, with Rp2 = 0.9487 and RPD = 4.4132. The three most important bands for UCS prediciton were identified as 1855.24 nm, 1240.29 nm, and 1589.91 nm respectively. Finally, comparison between the PSO-SVM-Bagging and CNN-LSTM algorithms revealed that the PSO-SVM-Bagging approach presented superior accuracy and generalization ability. This study validates the feasibility and scientific merit of applying HSI-based intelligent modeling as a NDT method for the UCS of CPB, providing a practical pathway for real-time monitoring, on-site feedback, and intelligent regulation of backfill performance in underground mining.
{"title":"A novel non-destructive testing method for the uniaxial compressive strength of cemented paste backfill based on hyperspectral imaging and artificial intelligence","authors":"Qing Na , Qiusong Chen , Daolin Wang , Jilong Pan , Yunbo Tao , Aixiang Wu","doi":"10.1016/j.ndteint.2026.103654","DOIUrl":"10.1016/j.ndteint.2026.103654","url":null,"abstract":"<div><div>Accurate and rapid evaluation of the uniaxial compressive strength (UCS) of cemented paste backfill (CPB) is crucial for ensuring safe and efficient underground mining. Traditional UCS tests, however, are contact-based, destructive and time-consuming, which severely limits real-time UCS monitoring and rapid feedback for in-situ backfill quality control. To overcome these limitations, this study innovatively introduces hyperspectral imaging (HSI) technology into the real-time, in-situ UCS monitoring, establishing a non-destructive testing (NDT) method by integrating HSI with artificial intelligence. A total of 120 CPB groups with five mass concentrations (61–73 %) and eight curing ages (3–28 d) were tested to obtain both UCS and corresponding hyperspectral data. The spectral response characteristics of the CPB under varying concentrations were analyzed, revealing that the reflectance gradually increased with concentration, and two distinct absorption peaks were observed near 1400 nm and 1950 nm. Two-dimensional correlation spectroscopy indicated that, under concentration interference, the spectral sensitivity was highest at 1900 nm and lowest at 1600 nm. Subsequently, the effect of various spectral preprocessing techniques and feature extraction algorithms on UCS prediction accuracy was investigated using the PSO-SVM-Bagging algorithm. The results demonstrated that the 2nd D-SG-Nor + UVE model exhibited the best performance, with <em>R</em><sub><em>p</em></sub><sup>2</sup> = 0.9487 and RPD = 4.4132. The three most important bands for UCS prediciton were identified as 1855.24 nm, 1240.29 nm, and 1589.91 nm respectively. Finally, comparison between the PSO-SVM-Bagging and CNN-LSTM algorithms revealed that the PSO-SVM-Bagging approach presented superior accuracy and generalization ability. This study validates the feasibility and scientific merit of applying HSI-based intelligent modeling as a NDT method for the UCS of CPB, providing a practical pathway for real-time monitoring, on-site feedback, and intelligent regulation of backfill performance in underground mining.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103654"},"PeriodicalIF":4.5,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-17DOI: 10.1016/j.ndteint.2026.103644
Kang An, Changyou Li
Microwave time reversal based on time integrated energy method (TIEM) was used for the detection of extended defects through combining time-resolved information from multiple sources. However, the wrong localization problem caused by the strong reflection from metal still appears when it is applied for non-destructive testing of multilayer composites backed by metal. In this paper, an improved TIEM (ITIEM) is proposed by properly combining TIEM and the time constraint information obtained from target initial reflection method to overcome the wrong localization problem and ensure the correct localization. Then, microwave time reversal with multiple sources based on ITIEM (ITIEM-MS-MTR) is proposed for the detection of extended cracks with different shapes, such as “V”-shaped crack and “W”-shaped crack. Its effectiveness and noise tolerance is proved through multiple investigations in two-dimensional cases. Furthermore, the proposed ITIEM-MS-MTR is investigated in the detection of extended defect in the multilayer composite skin of an aircraft wing model, and its effectiveness and noise tolerance is finally validated in 2D and 3D cases.
基于时间积分能量法（TIEM）的微波时间反演，结合多源时间分辨信息，对扩展缺陷进行检测。然而，在应用于金属背衬多层复合材料的无损检测时，由于金属的强反射，仍然存在定位错误的问题。本文将TIEM与目标初始反射法获得的时间约束信息合理结合，提出了一种改进的TIEM (ITIEM)，克服了错误定位问题，保证了正确定位。然后，提出了基于ITIEM的多源微波时间反演方法（ITIEM- ms - mtr），用于“V”型裂纹和“W”型裂纹等不同形状扩展裂纹的检测。通过对二维情况的多次研究，证明了该方法的有效性和耐噪性。最后，将所提出的ITIEM-MS-MTR方法应用于某飞机机翼模型多层复合材料蒙皮扩展缺陷的检测，并在二维和三维实例中验证了其有效性和噪声容限。
{"title":"An improved time integrated energy method for imaging extended defect in multilayer composites","authors":"Kang An, Changyou Li","doi":"10.1016/j.ndteint.2026.103644","DOIUrl":"10.1016/j.ndteint.2026.103644","url":null,"abstract":"<div><div>Microwave time reversal based on time integrated energy method (TIEM) was used for the detection of extended defects through combining time-resolved information from multiple sources. However, the wrong localization problem caused by the strong reflection from metal still appears when it is applied for non-destructive testing of multilayer composites backed by metal. In this paper, an improved TIEM (ITIEM) is proposed by properly combining TIEM and the time constraint information obtained from target initial reflection method to overcome the wrong localization problem and ensure the correct localization. Then, microwave time reversal with multiple sources based on ITIEM (ITIEM-MS-MTR) is proposed for the detection of extended cracks with different shapes, such as “V”-shaped crack and “W”-shaped crack. Its effectiveness and noise tolerance is proved through multiple investigations in two-dimensional cases. Furthermore, the proposed ITIEM-MS-MTR is investigated in the detection of extended defect in the multilayer composite skin of an aircraft wing model, and its effectiveness and noise tolerance is finally validated in 2D and 3D cases.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103644"},"PeriodicalIF":4.5,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-17DOI: 10.1016/j.ndteint.2026.103652
Yue Hu, Lijun Sun, Huailei Cheng, Ruikang Yang
Back-calculating pavement layer moduli from deflections is a key technique for evaluating in-service pavement performance, yet its reliability often declines for pavements with limited asphalt surface layer thickness (typically less than 18 cm). Through mechanistic analysis, this study identifies insufficient interlayer coordination between the surface and underlying base layer as the primary cause. The significant modulus differences lead to discontinuous interlayer deformation, deviating from the full continuity assumption of conventional models. To resolve this, a method inspired by the partial-continuous interlayer modeling approach in multi-layer elastic theory was introduced. An interlayer stiffness coordination factor Kv was defined to quantify the degree of interlayer synergy, and this parameter was incorporated into the SimuAPSO back-calculation software. Using measured deflection data and laboratory dynamic modulus tests, Kv values were determined across various pavement structures. Regression analysis revealed asphalt layer thickness and surface temperature as the dominant influencing variables, and the developed predictive model demonstrated strong robustness and statistical stability. Results indicate that when Kv reaches 106 MPa/cm, the interface behaves as fully coordinated. Furthermore, Kv increases with both asphalt layer thickness and surface temperature, revealing the combined influence of structural and environmental factors on interlayer mechanical behavior. Finally, validation using Long-Term Pavement Performance (LTPP) database and measured data from a Chinese highway section shows that incorporating the interlayer stiffness coordination mechanism markedly enhances the accuracy and stability of back-calculated moduli for the pavements, providing a practical framework for improved pavement evaluation.
{"title":"Modulus back-calculation method for asphalt pavements with limited surface layer thickness based on interlayer stiffness coordination factors","authors":"Yue Hu, Lijun Sun, Huailei Cheng, Ruikang Yang","doi":"10.1016/j.ndteint.2026.103652","DOIUrl":"10.1016/j.ndteint.2026.103652","url":null,"abstract":"<div><div>Back-calculating pavement layer moduli from deflections is a key technique for evaluating in-service pavement performance, yet its reliability often declines for pavements with limited asphalt surface layer thickness (typically less than 18 cm). Through mechanistic analysis, this study identifies insufficient interlayer coordination between the surface and underlying base layer as the primary cause. The significant modulus differences lead to discontinuous interlayer deformation, deviating from the full continuity assumption of conventional models. To resolve this, a method inspired by the partial-continuous interlayer modeling approach in multi-layer elastic theory was introduced. An interlayer stiffness coordination factor <em>K</em><sub><em>v</em></sub> was defined to quantify the degree of interlayer synergy, and this parameter was incorporated into the SimuAPSO back-calculation software. Using measured deflection data and laboratory dynamic modulus tests, <em>K</em><sub><em>v</em></sub> values were determined across various pavement structures. Regression analysis revealed asphalt layer thickness and surface temperature as the dominant influencing variables, and the developed predictive model demonstrated strong robustness and statistical stability. Results indicate that when <em>K</em><sub><em>v</em></sub> reaches 10<sup>6</sup> MPa/cm, the interface behaves as fully coordinated. Furthermore, <em>K</em><sub><em>v</em></sub> increases with both asphalt layer thickness and surface temperature, revealing the combined influence of structural and environmental factors on interlayer mechanical behavior. Finally, validation using Long-Term Pavement Performance (LTPP) database and measured data from a Chinese highway section shows that incorporating the interlayer stiffness coordination mechanism markedly enhances the accuracy and stability of back-calculated moduli for the pavements, providing a practical framework for improved pavement evaluation.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103652"},"PeriodicalIF":4.5,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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