Journal of Nondestructive Evaluation最新文献_第2页

Comparative Study of Non-Destructive Techniques for Wood Quality Assessment: Acoustic Tomography versus CT Scanning 木材质量无损评价技术的比较研究：声学断层扫描与CT扫描

IF 3 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-09-27 DOI: 10.1007/s10921-026-01432-9

Miloš Gejdoš, Katarína Michajlová, Tomáš Gergeľ

{"title":"Comparative Study of Non-Destructive Techniques for Wood Quality Assessment: Acoustic Tomography versus CT Scanning","authors":"Miloš Gejdoš, Katarína Michajlová, Tomáš Gergeľ","doi":"10.1007/s10921-026-01432-9","DOIUrl":"10.1007/s10921-026-01432-9","url":null,"abstract":"<div><p>This study evaluates and compares advanced non‑destructive techniques for wood quality assessment, focusing on acoustic stress‑wave tomography and X‑ray computed tomography (CT), complemented by digital image analysis and manual measurements according to EN 1309‑3. Acoustic tomography was applied to a dataset of 165 standing trees (oak, lime, pine, fir) to identify spatial patterns of internal degradation, while CT scanning of a subset of 20 harvested logs was used for direct quantitative validation. Statistical analyses included Kruskal–Wallis tests, correlation analysis, and multiple regression models. Significant differences in defect proportion were found among species (H = 34.64; <i>p</i> < 0.0001), together with a strong negative association between stem diameter and relative defect area (r ≈ − 0.67; p ≪ 0.001). In the CT‑validated subset, acoustic tomography showed a systematic but modest positive bias relative to CT (mean difference ≈ 4.1 percentage points), whereas digital photography consistently underestimated internal damage. Measurements according to EN 1309‑3 yielded generally conservative estimates, with occasional overestimation arising from geometric assumptions. Although the CT‑based validation is limited by the size of the harvested subset, the results confirm that CT provides the most accurate quantitative reference, while acoustic tomography is well suited for rapid in‑field screening and defect localization, particularly when supported by targeted verification methods (e.g. resistance drilling). The findings highlight the complementary roles of these techniques and support integrated workflows for practical wood quality assessment and decision‑making in forestry and timber processing.</p><h3>Graphical Abstract</h3>\u0000<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

AI-Driven Bridge Crack Detection: A Review of Deep Learning Applications for Durability Assessment 人工智能驱动的桥梁裂缝检测：深度学习在耐久性评估中的应用综述

IF 3 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-09-27 DOI: 10.1007/s10921-026-01437-4

Fangyuan Li, Md Sohel Rana

{"title":"AI-Driven Bridge Crack Detection: A Review of Deep Learning Applications for Durability Assessment","authors":"Fangyuan Li, Md Sohel Rana","doi":"10.1007/s10921-026-01437-4","DOIUrl":"10.1007/s10921-026-01437-4","url":null,"abstract":"<div><p>Bridge crack detection is paramount for structural health monitoring and durability assessment, yet traditional methods remain labor-intensive, subjective, and limited in accuracy. This review comprehensively examines the paradigm shift toward artificial intelligence (AI), particularly deep learning (DL), in automating both crack detection and quantification for bridge infrastructure. We systematically analyze state-of-the-art DL methodologies, categorizing convolutional neural networks (CNNs) as the foundational backbone for specialized architectures, including ‘you only look once’ (YOLO) variants for detection, U-Net architectures for segmentation, and generative adversarial networks (GANs) for data augmentation. The review highlights their superior performance in accuracy (> 95%), robustness to environmental variability, and real-time processing capabilities. Critical challenges such as data scarcity, annotation inconsistencies regarding crack severity, poor cross-environment generalization, computational constraints for edge deployment, and model interpretability gaps are rigorously evaluated. The paper also identifies key limitations in existing datasets, benchmarking practices, and regulatory integration. Finally, future directions are outlined with an emphasis on data-centric innovations, lightweight model optimization, explainable AI, and multimodal systems capable of predictive durability analysis. This synthesis underscores DL's transformative potential in enhancing bridge safety and calls for standardized frameworks to bridge the gap between research and real-world deployment.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Investigation of Acoustic-emission-based Method for Weld Damage Monitoring in High Temperature Coke Drum 基于声发射的高温焦炭转炉焊缝损伤监测方法研究

IF 3 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-09-27 DOI: 10.1007/s10921-026-01438-3

Zhihai Hu, Keqin Ding, Jianfang Zhou, Wei Xin, Li Chen, Song Liu, Guosong Wu

{"title":"Investigation of Acoustic-emission-based Method for Weld Damage Monitoring in High Temperature Coke Drum","authors":"Zhihai Hu, Keqin Ding, Jianfang Zhou, Wei Xin, Li Chen, Song Liu, Guosong Wu","doi":"10.1007/s10921-026-01438-3","DOIUrl":"10.1007/s10921-026-01438-3","url":null,"abstract":"<div><p>The safety monitoring of welds on high-temperature equipment has long been a persistent challenge within the petrochemical industry. The coke drum is a critical component in coke production, enduring prolonged cyclic exposure to high temperatures and mechanical loads. This makes its weld zone particularly susceptible to cracking, posing a major threat to safe and stable operation. To address this, this paper introduces, for the first time, an acoustic emission (AE) based method for real-time online monitoring of weld damage in in-service high-temperature coke drums. Given the large data volume and low processing efficiency of conventional waveform analysis, a practical three-step feature value screening method rooted in AE feature analysis is proposed. This method, using amplitude as a representative single feature parameter, not only effectively identifies the presence of weld cracks but also enables approximate localization of longer cracks; however, its capability in detecting small cracks remains somewhat limited. Nevertheless, the method compresses the data volume to merely 1.1% of its original size, thereby significantly improving monitoring efficiency. The effectiveness of this AE-based monitoring approach was subsequently validated on the inner wall of a coke drum. Overall, this study provides a simple and efficient engineering solution for identifying weld damage in coke drums under high-temperature conditions, which holds considerable practical significance for advancing equipment safety monitoring.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

A Quantum Tunneling-Driven Enhancement Framework for Unifying Computed Radiography and Digital Radiography in Nondestructive Testing 无损检测中统一计算机射线照相和数字射线照相的量子隧道驱动增强框架

IF 3 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-09-23 DOI: 10.1007/s10921-026-01420-z

Guancheng Lu, Guiqing Li, Jihua Guan, Jin Wei, Zhen Dong, Wei Wei

{"title":"A Quantum Tunneling-Driven Enhancement Framework for Unifying Computed Radiography and Digital Radiography in Nondestructive Testing","authors":"Guancheng Lu, Guiqing Li, Jihua Guan, Jin Wei, Zhen Dong, Wei Wei","doi":"10.1007/s10921-026-01420-z","DOIUrl":"10.1007/s10921-026-01420-z","url":null,"abstract":"<div><p>Robust cross-modal enhancement between computed radiography (CR) and digital radiography (DR) is a persistent challenge in industrial nondestructive testing (NDT) because conventional methods inevitably sacrifice information during dynamic range adaptation, thereby compromising detection reliability. To overcome this limitation, a rigorous quantum–mechanical framework is established by drawing a formal analogy between image detail enhancement and quantum tunneling. Pixel intensity distributions are mapped onto quantum probability densities, and quantum potential barriers are systematically derived from normalized gradient tensor fields. This physically grounded methodology yields an enhancement operator derived entirely from first principles without relying on training data, seamlessly unifying CR and DR processing within a single paradigm. Experimental evaluation on 60 industrial X-ray images demonstrates that the proposed framework achieves a structural similarity index (SSIM) of 0.92 (CR) and 0.96 (DR), a peak signal-to-noise ratio (PSNR) of 28.39 dB (CR) and 38.80 dB (DR), and average relative improvements in spatial frequency (SF) of 32.3% and 18.4% for CR and DR cases, respectively. This study establishes a novel quantum–mechanical enhancement paradigm and provides a unified mathematical foundation for cross-modal radiographic processing. The demonstrated gains in defect detection sensitivity and cross-modal consistency underscore the potential of the proposed framework to transform computational imaging practices in NDT. The source code accompanying this study is available at the following persistent identifier: https://doi.org/10.5281/zenodo.21493535.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149034940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Edge AI for Embedded Acoustic Emission Clustering: A Globally Optimal Sequential Mixture Model for Non-Destructive Structural Health Monitoring 嵌入式声发射聚类的边缘人工智能：一种用于无损结构健康监测的全局最优序列混合模型

IF 3 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-09-23 DOI: 10.1007/s10921-026-01431-w

Lina-Nadjeh Labar, Ikram Remadna, Oualid Laiadi, Noureddine Zerhouni

{"title":"Edge AI for Embedded Acoustic Emission Clustering: A Globally Optimal Sequential Mixture Model for Non-Destructive Structural Health Monitoring","authors":"Lina-Nadjeh Labar, Ikram Remadna, Oualid Laiadi, Noureddine Zerhouni","doi":"10.1007/s10921-026-01431-w","DOIUrl":"10.1007/s10921-026-01431-w","url":null,"abstract":"<div><p>Structural Health Monitoring (SHM) using Acoustic Emission (AE) as a Non-Destructive Testing modality requires continuous online classification of transient hits into damage regimes that appear sequentially as a structure degrades. The state-of-the-art GMMSEQ model couples a Gaussian mixture with time-varying sigmoid mixing weights, but its Expectation–Maximization (EM) procedure is initialized by a time-blind GMM, making it prone to local optima. We propose Dynamic-Programming GMMSEQ <b>DP-GMMSEQ</b>, a two-stage algorithm that replaces this initialization with a globally optimal hard-boundary segmentation obtained by dynamic programming (global optimality applies to this initialization stage), then refines it with standard GMMSEQ EM. On the public ORION-AE bolt-loosening benchmark, DP-GMMSEQ raises the Adjusted Rand Index (ARI) over the original GMMSEQ on four of five campaigns, with absolute gains up to <span>(14.9%)</span>. We further present the first end-to-end embedded AE pipeline on an STM32 microcontroller as a proof-of-concept foundational architecture, covering high-pass filtering, db45 wavelet denoising, hit detection, 32-feature extraction and on-device inference. The DP-GMMSEQ inference stage achieves a per-hit latency of 450 µs with an average performance drop of only <span>(sim !13,%)</span> across precision, recall, entropy and ARI compared to the desktop baseline. The dominant remaining bottleneck is the wavelet pre-processing front-end, not the statistical clustering itself, a finding that identifies the signal-processing stage as the key target for future real-time optimization.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149034521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

AdaMorphNet: Morphology-Aware Weakly-Supervised Defect Localization for Nondestructive Radiographic Weld Inspection 基于形态感知的弱监督缺陷定位技术

IF 3 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-09-23 DOI: 10.1007/s10921-026-01433-8

Thanh-Cong Truong

{"title":"AdaMorphNet: Morphology-Aware Weakly-Supervised Defect Localization for Nondestructive Radiographic Weld Inspection","authors":"Thanh-Cong Truong","doi":"10.1007/s10921-026-01433-8","DOIUrl":"10.1007/s10921-026-01433-8","url":null,"abstract":"<div><p>High annotation costs and the difficulty of localizing fine-grained linear defects limit the deployment of deep learning in automated nondestructive evaluation (NDE) of welds. Weakly-Supervised Object Localization (WSOL) reduces labeling effort, but standard Class Activation Map (CAM) approaches produce diffuse heatmaps that do not resolve the morphology of thin welding discontinuities. We introduce AdaMorphNet, a framework pairing multi-scale CAM fusion with defect-specific geometric constraints drawn from radiographic inspection standards. In place of uniform thresholding, the model refines coarse activations with one constraint per defect class: topology-preserving thresholding for cracks, an anisotropic height limit for lack of penetration, and distribution-adaptive clustering for porosity. On the RIAWELC benchmark AdaMorphNet reaches 99.96% classification accuracy. Against 1,840 manually verified ground-truth boxes it attains 56.81% mean IoU and 97.88% Pointing Accuracy, exceeding a standard Grad-CAM baseline by 20.81 percentage points; an architectural ablation attributes 8.07 points of mean IoU to multi-scale fusion over single-scale CAM under identical post-processing. The pipeline localizes 37.1 patches of <span>({224 times 224})</span> pixels per second on consumer hardware, which supports near-real-time pre-screening at an estimated 1.8 radiographs per second under a two-stage triage schedule, without dense pixel-level supervision.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149034528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Non-Contact Evaluation of Concrete Floor Abrasion Using 3D Optical Scanning 基于三维光学扫描的混凝土楼板磨损非接触评价

IF 3 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-09-18 DOI: 10.1007/s10921-026-01424-9

Artur Piekarczuk, Aleksandra Mazurek

{"title":"Non-Contact Evaluation of Concrete Floor Abrasion Using 3D Optical Scanning","authors":"Artur Piekarczuk, Aleksandra Mazurek","doi":"10.1007/s10921-026-01424-9","DOIUrl":"10.1007/s10921-026-01424-9","url":null,"abstract":"<div><p>This study investigates the applicability of non-contact 3D optical scanning for quantitative evaluation of abrasion damage in concrete floors and its consistency with the standard British Cement Association (BCA) abrasion test. Abrasion experiments were conducted under <i>in situ</i> conditions in accordance with the standard BCA abrasion test procedure, while surface wear was independently measured using a handheld metrology-grade 3D scanner supported by photogrammetric positioning and high-resolution local scanning. The acquired point cloud data were converted into triangular surface meshes and analysed using a mesh-to-mesh comparison approach, enabling spatially resolved assessment of abrasion depth. Fifteen measurement scenarios were examined to evaluate the influence of sampling density, point location strategy, and measurement technique. Statistical analysis based on the Shapiro–Wilk test and one-way ANOVA demonstrated that the observed variability of abrasion depth is governed primarily by heterogeneous surface damage rather than by the applied measurement method. The results show that 3D optical scanning provides abrasion-depth values consistent with conventional point-based measurements and does not affect abrasion-based material classification. Rather than replacing the standardized BCA procedure, the proposed approach complements its measurement stage by providing full-field geometric information that enables qualitative assessment of abrasion morphology, spatial variability and surface topography, thereby significantly enhancing the diagnostic capabilities of the test.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-026-01424-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148987129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multitask Learning Approach for Bolt Torque Loss Detection Using Variable-Frequency Hammering Cepstral Coefficients 基于变频锤击倒谱系数的螺栓扭矩损失检测多任务学习方法

IF 3 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-09-16 DOI: 10.1007/s10921-026-01426-7

Xi Huang, Huang Huang, Mao Kurumatani, Zhen Sun, Rui Pang, Zhishen Wu

{"title":"Multitask Learning Approach for Bolt Torque Loss Detection Using Variable-Frequency Hammering Cepstral Coefficients","authors":"Xi Huang, Huang Huang, Mao Kurumatani, Zhen Sun, Rui Pang, Zhishen Wu","doi":"10.1007/s10921-026-01426-7","DOIUrl":"10.1007/s10921-026-01426-7","url":null,"abstract":"<div><p>Bolt-size-dependent variations in hammering responses tend to affect the classification of acoustic bolt torque loss, thereby making direct transfer from source bolts to a different target bolt size difficult. To address this drawback, this study proposes a variable-frequency hammering cepstral coefficients (VFHCC)-based domain-balanced multitask learning long short-term memory (MTL-LSTM) framework for cross-size bolt torque-loss classification with limited target-bolt calibration. First, hammering cepstral coefficients (HCCs) were introduced as an alternative to conventional Mel-frequency cepstral coefficients (MFCCs). The HCC parameters were optimized using a particle swarm optimization support vector machine (PSO-SVM). Second, variable-frequency hammering was applied to enrich the acoustic representation prior to cepstral feature extraction. Third, a domain-balanced MTL-LSTM model was developed to combine M20/M28 source-bolt data with limited M24 calibration data. The dataset contained 1,650 one-second WAV recordings from M20, M24, and M28 bolts under 11 torque-loss classes. Under the strict source-only protocol, direct transfer from M20/M28 to M24 produced low classification performance, confirming a strong cross-size acoustic domain shift. Under the target-calibrated protocol, VFHCC substantially improved M24 classification compared with single-frequency features. With two M24 calibration samples per class, the proposed method achieved an accuracy of 0.9879 and a macro-F1 score of 0.9875 on the remaining M24 test samples. These results demonstrate that variable-frequency acoustic representation and domain-balanced learning provide an effective strategy for cross-size bolt torque-loss classification when limited target-bolt calibration data are available.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Efficient Improved SSA for 3D Geometric Information Recovery of Surface Defect in Eddy Current NDT 涡流无损检测中表面缺陷三维几何信息恢复的高效改进SSA

IF 3 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-09-16 DOI: 10.1007/s10921-026-01428-5

Yang Bao, Yiming Liu, Jiuhao Ge, Mingtai Yang, Fangfang Wang, Xiaokang Yin

{"title":"Efficient Improved SSA for 3D Geometric Information Recovery of Surface Defect in Eddy Current NDT","authors":"Yang Bao, Yiming Liu, Jiuhao Ge, Mingtai Yang, Fangfang Wang, Xiaokang Yin","doi":"10.1007/s10921-026-01428-5","DOIUrl":"10.1007/s10921-026-01428-5","url":null,"abstract":"<div><p>The quality of key equipment is important during its lifelong maintenance. Non-destructive testing (NDT) is a non-invasive technique which ensure the completeness of the equipment during the quality evaluation. In this article, an improved sparrow search algorithm (ISSA) is proposed for efficient 3D surface defect reconstruction in eddy current NDT. The proposed method optimizes the computational efficiency over the traditional SSA, which has the limitations that some individuals conduct redundant or invalid searches. It reduces the search efficiency of the algorithm in the face of high-dimensional search spaces during complex 3D surface defect reconstruction, which directly leads to the prolonged inversion time costs. To address this issue, the ISSA introduces a variable helix factor, adaptive weight, and improved boundary constraints, which aim at strengthening the scroungers’ global and local search capabilities at different stages, enhancing the population’s overall optimization ability, and improving the search effectiveness of boundary individuals. These improvements boost the search efficiency in high-dimensional spaces, enabling the method to achieve more efficient solutions to 3D inverse problems in ECNDT. Furthermore, for high-dimensional input features, the well-known full-factorial sampling (FFS) strategy requires many samples, which leads to high computational burdens in constructing the relationship between the responses and geometric parameters of the surface defects, whereas the Latin hypercube sampling (LHS) strategy effectively overcomes this issue by adopting a stratified sampling mechanism. Both the prediction accuracy and computational efficiency of the proposed method are validated by comparing the predicted defect geometric sizes with the ground truth through simulations and experiments. The results show that the proposed ISSA-based inverse solver provides a reliable solution for surface defect reconstruction in 3D ECNDT.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Slow Dynamics in Concrete: Effects of Temperature, Strength Variation, and Microcracking Damage 混凝土中的慢动力学：温度、强度变化和微裂损伤的影响

IF 3 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2026-09-16 DOI: 10.1007/s10921-026-01427-6

Clayton Malone, Jinying Zhu

{"title":"Slow Dynamics in Concrete: Effects of Temperature, Strength Variation, and Microcracking Damage","authors":"Clayton Malone, Jinying Zhu","doi":"10.1007/s10921-026-01427-6","DOIUrl":"10.1007/s10921-026-01427-6","url":null,"abstract":"<div><p>In this study, we investigated the slow dynamic behavior of concrete with varying compressive strengths (<span>(f'_c = 32sim 56)</span> MPa) and alkali-silica reaction (ASR) damage. Concrete prisms were conditioned by compressive loading and recovery was monitored by coda wave interferometry (CWI). A self-referencing temperature correction technique was used to minimize the effect of ambient temperature changes, demonstrating the importance of careful temperature control and correction. For intact specimens, both the recovery rate, <span>(m_v)</span>, and velocity drop magnitude, |<i>c</i>|, generally increased with compressive strength. Recovery times were calculated from the fit parameters, and higher strength specimens were found to recover faster (9.9 h for <span>(f'_c = 32)</span> MPa, 6.6 h for <span>(f'_c = 56)</span> MPa). When the same stress was applied, ASR-damaged specimens had increased softening and faster recovery rates, but longer recovery times (up to 458 h). These findings demonstrate the potential of slow dynamics in concrete characterization, through its ability to evaluate the strength of intact samples and its sensitivity to microcracking in damaged samples.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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