Ndt & E International最新文献_第2页

Data-driven approach for water-to-cement ratio prediction in fresh cement paste from raw EIS measurements 基于原始EIS测量的新水泥浆水灰比预测数据驱动方法

IF 4.5 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2026-02-04 DOI: 10.1016/j.ndteint.2026.103664

Joohye Park, Jinyoung Hong, Junyoung Lee, Hajin Choi

Maintaining a consistent water-to-cement (w/c) ratio is critical for the strength development and long-term durability of cementitious materials; however, reliable on-site assessment remains challenging due to environmental variability and uncontrolled water addition. This study proposes a non-destructive, data-driven approach for directly estimating the w/c ratio of fresh cement paste by integrating electrochemical impedance spectroscopy (EIS) with a Gradient Boosting model. A total of 538 impedance spectra were collected under controlled laboratory conditions across a w/c range of 0.30–0.45 at early hydration stages. Raw impedance features measured within the 250 kHz–1 Hz frequency range were analyzed without relying on equivalent circuit fitting, and the proposed model achieved a prediction accuracy of up to R² = 0.85. Statistical preprocessing using median absolute deviation (MAD) filtering improved spectral stability, while frequency-window specification was shown to be critical for robust w/c estimation. SHapley Additive exPlanations (SHAP) analysis further revealed that the imaginary impedance component (

Z^{″}

) and the frequency region near 1 kHz dominate the model predictions, reflecting sensitivity to interfacial polarization and ionic relaxation processes associated with early-age microstructural conditions. The proposed EIS–machine learning framework enables a rapid and physically interpretable estimation of the w/c ratio at the paste scale and provides a foundation for future extension to mortar and concrete for practical quality control applications.

保持一致的水灰比（w/c）对于胶凝材料的强度发展和长期耐久性至关重要；然而，由于环境变化和不受控制的水添加，可靠的现场评估仍然具有挑战性。本研究提出了一种非破坏性的、数据驱动的方法，通过将电化学阻抗谱（EIS）与梯度增强模型相结合，直接估计新鲜水泥浆体的w/c比。在控制的实验室条件下，在0.30 ~ 0.45的w/c范围内，共收集了538个水化早期阻抗谱。在不依赖等效电路拟合的情况下，对250 kHz-1 Hz频率范围内测量的原始阻抗特征进行了分析，所提出的模型的预测精度高达R2 = 0.85。使用中位数绝对偏差（MAD）滤波的统计预处理提高了频谱稳定性，而频率窗规格对于稳健的w/c估计至关重要。SHapley加性解释（SHAP）分析进一步表明，想象阻抗分量（Z″）和接近1 kHz的频率区域主导了模型预测，反映了与早期微观结构条件相关的界面极化和离子弛豫过程的敏感性。提出的eis机器学习框架能够快速且物理上可解释地估计膏体尺度的w/c比，并为将来扩展到砂浆和混凝土的实际质量控制应用提供基础。

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引用次数: 0

Effective damage detection method based on electromechanical impedance and parallel connection of the transducers 基于机电阻抗和传感器并联的有效损伤检测方法

IF 4.5 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2026-02-03 DOI: 10.1016/j.ndteint.2026.103667

Shishir Kumar Singh , Paweł H. Malinowski

Several research studies aim to employ the electromechanical impedance method (EMI) for effective health monitoring. At the same time, limited studies focused on increasing damage detection efficiency using a combination of sensors under noise and temperature variation. This novel research aims to outperform the temperature compensation algorithm development by using a robust multiple-sensor instrumentational strategy for damage detection in structures. This research combines EMI resistance data in parallel connection for damage detection in the steel beam structure. The resistance parameters based on parallel combinations are studied and compared with the output of single transducers or series connections for the added simulated mass, and simulated cracks with variations of the temperature conditions. The performance comparison has been made in the selected frequency range of 1–100 kHz for the additional mass and 30-80 kHz for the simulated cracked steel beam. The damage sensitivity-based performance comparison has been studied using the root mean square deviation (RMSD) index. The resistance data fusion-based parallel connection has shown a better performance of damage detection capability over a single actuator or a series of connected actuators in varying environmental temperature conditions for the real crack and simulated added mass. The simulated added mass and crack are successfully detected at a higher temperature in the case of the parallel combination of the actuators.

一些研究旨在利用机电阻抗法（EMI）进行有效的健康监测。与此同时，有限的研究集中在噪声和温度变化下使用传感器组合来提高损伤检测效率。这项新颖的研究旨在通过使用鲁棒的多传感器仪器策略来进行结构损伤检测，从而超越温度补偿算法的发展。本研究结合并联连接的电磁干扰电阻数据，对钢梁结构进行损伤检测。研究了基于并联组合的电阻参数，并将其与单换能器或串联连接输出的模拟质量和模拟裂纹随温度条件的变化进行了比较。在1 ~ 100 kHz的频率范围内对附加质量和30 ~ 80 kHz的频率范围内对模拟裂纹钢梁进行了性能比较。采用均方根偏差（RMSD）指标研究了基于损伤敏感性的性能比较。在不同的环境温度条件下，基于电阻数据融合的并行连接对真实裂纹和模拟附加质量的损伤检测能力优于单个或串联的执行器。在执行器并联组合的情况下，在更高的温度下成功地检测到模拟的附加质量和裂纹。

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引用次数: 0

Seismic anisotropy analysis across a vertical crack in concrete using a controllable high-frequency source 利用可控高频震源分析混凝土垂直裂缝的地震各向异性

IF 4.5 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2026-01-30 DOI: 10.1016/j.ndteint.2026.103651

Nontawat Srisapan, Sananda Ray, Gregory P. Waite, Roohollah Askari

The reliable detection of internal defects in concrete structures remains challenging in nondestructive testing (NDT). Conventional seismic sources, such as hammers or weight drops, lack repeatability and cannot generate sufficiently high frequencies for detailed defect characterization. This research addresses these issues by developing the Seesaw Hammer, a repeatable seismic source capable of generating controlled high-frequency seismic waves. Lab experiments verified its repeatability and frequency tunability by varying tip weight and stiffness, where lighter and stiffer tips produced higher frequencies. Thus, it is suitable for guided wave analysis in concrete structures. The Seesaw Hammer was tested on a concrete slab containing a vertical crack, using two configurations: parallel and perpendicular arrays relative to the crack orientation. Data was acquired for crack filled with air, water, and polyethylene glycol (PEG), a customized viscous fluid. Phase velocity and quality factor (Q-factor) analysis shows perpendicular array configuration resulting in lower Q-factor and phase velocity. The degree of seismic anisotropy (dependence of seismic properties on array orientation relative to the crack) varied significantly with fluid type. Air-filled cracks exhibited the highest anisotropy and lowest Q-factor, whereas PEG-filled cracks demonstrated the lowest anisotropy and highest Q-factor, indicating that seismic anisotropy analysis using the developed high-frequency seismic source effectively identifies and characterizes vertical cracks, as well as the fluids contained within them.

混凝土结构内部缺陷的可靠检测一直是无损检测的难点。传统的震源，如锤击或重量下降，缺乏可重复性，不能产生足够高的频率来详细描述缺陷。该研究通过开发跷跷板锤来解决这些问题，跷跷板锤是一种可重复的地震源，能够产生可控的高频地震波。实验室实验通过改变尖端重量和刚度来验证其重复性和频率可调性，其中更轻和更硬的尖端产生更高的频率。因此，它适用于混凝土结构中的导波分析。跷跷板锤在含有垂直裂缝的混凝土板上进行了测试，使用了两种配置：相对于裂缝方向的平行和垂直阵列。数据采集了填充空气、水和聚乙二醇（PEG）（一种定制的粘性流体）的裂缝。相速度和质量因子（q因子）分析表明，垂直阵列结构可以降低q因子和相速度。地震各向异性的程度（地震性质与相对于裂缝的阵列方向的依赖）随着流体类型的不同而显著变化。充气裂缝各向异性最高，q因子最低，而peg填充裂缝各向异性最低，q因子最高，表明利用已开发的高频震源进行地震各向异性分析可以有效识别和表征垂直裂缝及其所含流体。

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引用次数: 0

Velocity field inversion for heterogeneous concrete and recognition of near-surface rebar using ultrasonic waves 非均质混凝土的速度场反演及近表面钢筋的超声识别

IF 4.5 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2026-01-29 DOI: 10.1016/j.ndteint.2026.103657

Yunfei Zou , Zhiyong Xu , Zijian Wang , Zhishen Wu

The Total Focusing Method (TFM) is widely used for imaging and recognizing the interior of underwater concrete. However, the TFM relies on known wave velocities and cannot invert material properties, resulting in resolution limitations. To this end, this study presents an improved Full Waveform Inversion (FWI) approach that can invert the velocity field characterizing heterogeneous concrete, enabling accurate recognition of rebars near the surface. First, the polarization characteristics of shear horizontal waves are used to suppress the mode conversion of ultrasounds, thereby simplifying the wavefield. Second, source signal estimation combined with a parabolic search algorithm is implemented to iteratively update the optimal source wavelet and step size, improving inversion stability. Third, a weighting function is introduced to suppress interference from interfacial waves, thereby enabling the inversion of high-resolution velocity fields. Additionally, a detection method is proposed for accurate recognition of rebars and structural interfaces. Experimental results demonstrate that the improved FWI method outperforms traditional TFM in both localization and diameter accuracy. Specifically, the mean error of rebar localization is reduced from 1.76 cm to 0.27 cm, while the mean error of diameter decreases from 0.94 cm to 0.36 cm. These advancements extend the application of FWI from seismic wavefield to underwater concrete, advancing the fields of non-destructive testing and structural health monitoring.

全聚焦法（TFM）广泛用于水下混凝土内部的成像和识别。然而，TFM依赖于已知的波速，不能反转材料特性，导致分辨率限制。为此，本研究提出了一种改进的全波形反演（FWI）方法，该方法可以反演表征非均质混凝土的速度场，从而准确识别地表附近的钢筋。首先，利用剪切水平波的偏振特性抑制超声波的模式转换，从而简化波场；其次，将源信号估计与抛物线搜索算法相结合，迭代更新最优源小波和步长，提高反演稳定性；第三，引入加权函数抑制界面波的干扰，从而实现高分辨率速度场的反演。此外，提出了一种准确识别钢筋和结构界面的检测方法。实验结果表明，改进的FWI方法在定位和直径精度上都优于传统的TFM方法。其中，钢筋定位的平均误差从1.76 cm减小到0.27 cm，直径的平均误差从0.94 cm减小到0.36 cm。这些进展将FWI的应用从地震波场扩展到水下混凝土，推动了无损检测和结构健康监测领域的发展。

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引用次数: 0

Characterization of localized porosity in laminated composites using ultrasonic interferometry 用超声干涉测量法表征层合复合材料的局部孔隙度

IF 4.5 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2026-01-28 DOI: 10.1016/j.ndteint.2026.103658

William Lucas , Florence Saffar , Tony Valier-Brasier , Alverède Simon

Localized porosity in stratified carbon fiber–reinforced polymer (CFRP) composites can severely affect mechanical performance and structural integrity. Conventional ultrasonic attenuation methods can quantify residual porosity but cannot characterize clusters of voids. This work presents an ultrasonic interferometric approach for the quantitative assessment of localized porosity in laminated composites. CFRP specimens are manufactured in an autoclave under controlled conditions to introduce defined porosity levels. Through-transmission ultrasonic measurements are compared with an analytical multilayer propagation model including a degraded layer described by multiple scattering theory. Solving the inverse problem enabled estimation of both the location and concentration of void clusters.

Results show good agreement with X-ray tomographies, confirming the capability of ultrasonic interferometry for accurate detection of localized porosity and its potential for non-destructive evaluation of laminated composites.

层状碳纤维增强聚合物（CFRP）复合材料的局部孔隙严重影响其力学性能和结构完整性。传统的超声衰减方法可以量化残余孔隙度，但不能表征孔隙簇。本文提出了一种超声干涉法定量评价层合复合材料局部孔隙度的方法。CFRP试样在受控条件下在高压灭菌器中制造，以引入定义的孔隙率水平。将透射超声测量结果与包含退化层的多层解析传播模型进行了比较。通过求解逆问题，可以估计空穴团的位置和浓度。结果与x射线层析成像结果吻合良好，证实了超声干涉测量法精确检测局部孔隙的能力，以及其对层合复合材料无损评价的潜力。

引用次数: 0

Invariants in Eddy Current Testing via dimensional analysis 量纲分析涡流检测中的不变量

IF 4.5 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2026-01-27 DOI: 10.1016/j.ndteint.2026.103661

Vincenzo Mottola , Alessandro Sardellitti , Filippo Milano , Luigi Ferrigno , Marco Laracca , Antonello Tamburrino

The Buckingham’s π theorem has been recently introduced in the context of Non destructive Testing & Evaluation (NdT&E) , giving a theoretical basis for developing simple but effective methods for multi-parameter estimation via dimensional analysis. Dimensional groups, or π-groups, allow for the reduction of the number of parameters affecting the dimensionless measured quantities.

In many real-world applications, the main interest is in estimating only a subset of the variables affecting the measurements. An example is estimating the thickness and electrical conductivity of a plate from Eddy Current Testing data, regardless of the lift-off of the probe, which may be either uncertain and/or variable. Alternatively, one may seek to estimate thickness and lift-off while neglecting the influence of the electrical conductivity, or to estimate the electrical conductivity and the lift-off, neglecting the thickness.

This is where the concept of invariants becomes crucial. An invariant transformation is a mathematical mapping or a specific operating condition that makes the measured signal independent of one or more of these uncertain parameters. Invariant transformations provide a way to isolate useful signals from uncertain ones, improving the accuracy and reliability of the NdT results.

The main contribution of this paper is a systematic method to derive invariant transformations for frequency domain Eddy Current Testing data, via dimensional analysis. The proposed method is compatible with real-time and in-line operations.

After its theoretical foundation is introduced, the method is validated by means of experimental data, with reference to configurations consisting of plates with different thicknesses, electrical conductivity, and lift-off. The experimental validation proves the effectiveness of the method in achieving excellent accuracy on a wide range of parameters of interest.

白金汉π定理最近在无损检测和评估（NdT&；E）的背景下被引入，为通过量纲分析开发简单而有效的多参数估计方法提供了理论基础。量纲群，或π群，允许减少影响无量纲测量量的参数数量。在许多实际的应用程序中，主要的兴趣是估计影响测量的变量的子集。一个例子是根据涡流测试数据估计板的厚度和电导率，而不考虑探头的升力，这可能是不确定的和/或可变的。或者，人们可以在忽略电导率影响的同时估计厚度和上升，或者在忽略厚度的情况下估计电导率和上升。这就是不变量的概念变得至关重要的地方。不变变换是一种数学映射或一种特定的操作条件，它使被测信号与这些不确定参数中的一个或多个无关。不变变换提供了一种从不确定信号中分离有用信号的方法，提高了无损检测结果的准确性和可靠性。本文的主要贡献是通过量纲分析，给出了一种系统的方法来推导频域涡流检测数据的不变变换。该方法兼容实时和在线操作。在介绍了该方法的理论基础后，通过实验数据对该方法进行了验证，并参考了由不同厚度、电导率和升力组成的板的配置。实验验证了该方法的有效性，在广泛的感兴趣参数范围内取得了优异的精度。

{"title":"Invariants in Eddy Current Testing via dimensional analysis","authors":"Vincenzo Mottola , Alessandro Sardellitti , Filippo Milano , Luigi Ferrigno , Marco Laracca , Antonello Tamburrino","doi":"10.1016/j.ndteint.2026.103661","DOIUrl":"10.1016/j.ndteint.2026.103661","url":null,"abstract":"<div><div>The Buckingham’s π theorem has been recently introduced in the context of Non destructive Testing & Evaluation (NdT&E) , giving a theoretical basis for developing simple but effective methods for multi-parameter estimation via <em>dimensional analysis</em>. Dimensional groups, or π-groups, allow for the reduction of the number of parameters affecting the dimensionless measured quantities.</div><div>In many real-world applications, the main interest is in estimating only a subset of the variables affecting the measurements. An example is estimating the thickness and electrical conductivity of a plate from Eddy Current Testing data, regardless of the lift-off of the probe, which may be either uncertain and/or variable. Alternatively, one may seek to estimate thickness and lift-off while neglecting the influence of the electrical conductivity, or to estimate the electrical conductivity and the lift-off, neglecting the thickness.</div><div>This is where the concept of invariants becomes crucial. An invariant transformation is a mathematical mapping or a specific operating condition that makes the measured signal independent of one or more of these uncertain parameters. Invariant transformations provide a way to isolate useful signals from uncertain ones, improving the accuracy and reliability of the NdT results.</div><div>The main contribution of this paper is a systematic method to derive <em>invariant</em> transformations for frequency domain Eddy Current Testing data, via dimensional analysis. The proposed method is compatible with real-time and in-line operations.</div><div>After its theoretical foundation is introduced, the method is validated by means of experimental data, with reference to configurations consisting of plates with different thicknesses, electrical conductivity, and lift-off. The experimental validation proves the effectiveness of the method in achieving excellent accuracy on a wide range of parameters of interest.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103661"},"PeriodicalIF":4.5,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189551","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}

引用次数: 0

Remote field eddy current monitoring of hole-edge cracks in bolted joints: Theoretical modeling and experimental validation 螺栓连接孔边裂纹远场涡流监测：理论建模与实验验证

IF 4.5 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2026-01-27 DOI: 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.

远程场涡流（RFEC）测试为地下检测提供了深穿透能力，但其在受限多层几何结构（如螺栓连接）中的应用仍未开发。本研究提出了一种将柔性涡流传感器集成与解析和有限元建模相结合的嵌入式RFEC方法，以阐明螺栓连接内远程场的形成机制。系统分析了激励频率、材料性能和螺栓几何形状对RFEC耦合的影响。对铝合金螺栓连接的实验验证表明，在3 kHz激励下，该传感器可监测裂纹深度达10 mm，对应的振幅和相位变化分别为53 μV和0.55°。根据传感器信号特征，可以监测裂缝的位置、长度和深度。研究结果验证了嵌入式RFEC方法的可行性和高灵敏度，将RFEC应用于复杂的结构健康监测场景。

引用次数: 0

Application of AI-based techniques for concrete air permeability classification 基于人工智能技术在混凝土透气性分类中的应用

IF 4.5 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2026-01-27 DOI: 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。

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引用次数: 0

Propagation characteristics of acoustic emission signals across the cross-section of parallel wire strands 声发射信号在平行导线横截面上的传播特性

IF 4.5 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2026-01-27 DOI: 10.1016/j.ndteint.2026.103659

Zhitao Sun , Dongming Feng , Yixuan Zhao , Futang Wei

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.

为了提高斜拉桥平行线束损伤定位精度，优化声发射传感器布置，建立了声发射信号幅值沿平行线束截面衰减的解析模型。然后使用铅笔芯断裂（PLB）和中心冲孔冲击作为模拟损伤源进行衰减测试，然后进行灵敏度分析。试验结果与解析解的对比表明，解析模型更适合低频信号的分析，且随着信号频率的升高，偏差逐渐增大。分析模型和测试结果均表明，声发射信号的高频分量在PWS截面内衰减更快，谐振频率越低的传感器性能越好。随着声发射信号频率的增加，传播过程中的能量耗散也随之增加。当频率从5 kHz增加到100 kHz时，衰减系数和声阻抗比分别增加4.17倍和4.31倍。对于桥梁PWS的损伤监测，既要考虑传感器的谐振频率，也要考虑峰值信号能量，优先考虑谐振频率。

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引用次数: 0

Intelligent detection of pipeline girth weld defects: a non-destructive testing domain knowledge-integrated approach 管道环焊缝缺陷智能检测：一种无损检测领域知识集成方法

IF 4.5 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2026-01-24 DOI: 10.1016/j.ndteint.2026.103653

Yong Zhang , Hongquan Jiang , Huyue Cheng , Tianjun Liu , Yuhang Qiu , Deyan Yang , Peng Liu , Jianmin Gao , Zelin Zhi , Deqiang Jing , Xiaoming Zhang

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。

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引用次数: 0