Abstract
This pilot study presents a novel, automated, and scalable methodology for detecting and evaluating subsurface defect-prone regions in concrete slabs using Impact Echo (IE) signal analysis. The approach integrates advanced signal processing, clustering, and visual analytics to identify subsurface anomalies. A unique adaptive thresholding method tailors frequency-based defect identification to the distinct material properties of each slab. The methodology generates frequency maps, binary masks, and k-means cluster maps to automatically classify defect and non-defect regions. Key visualizations, including 3D surface plots, cluster maps, and contour plots, are employed to analyze spatial frequency distributions and highlight structural anomalies. The study utilizes a labeled dataset constructed at the Federal Highway Administration (FHWA) Advanced Sensing Technology Nondestructive Evaluation Laboratory. Evaluations involve ground-truth masking, comparing the generated defect maps with top-view binary masks derived from the information provided by the FHWA. The performance metrics, specifically F1-scores and AUC-ROC, achieve values of up to 0.95 and 0.83, respectively. The results demonstrate the robustness of the methodology, consistently identifying defect-prone areas with minimal false positives and few missed defects. Adaptive frequency thresholding ensures flexibility in addressing variations across slabs, providing a scalable framework for detecting structural anomalies. Additionally, the methodology is adaptable to other frequency-based signals due to its generalizable thresholding mechanism and holds potential for integrating multimodal sensor fusion. This automated and scalable pipeline minimizes manual intervention, ensuring accurate and efficient defect detection, further advancing Non-Destructive Evaluation (NDE) techniques.
Abstract (translated)
这项初步研究提出了一种新颖的、自动化的和可扩展的方法，用于通过冲击回波（IE）信号分析来检测和评估混凝土板中的潜在缺陷区域。该方法结合了先进的信号处理、聚类和可视化分析技术，以识别地下异常。一种独特的自适应阈值方法根据每块混凝土板特有的材料属性定制基于频率的缺陷识别。该方法生成频率图、二进制掩码和k-means聚类图，自动分类有缺陷和无缺陷区域。关键可视化工具包括3D表面图、聚类图和等高线图，用于分析空间频率分布并突出结构异常。本研究使用了在美国联邦公路管理局（FHWA）先进传感技术非破坏性评估实验室构建的标注数据集。评估涉及地面真值掩码，将生成的缺陷地图与从FHWA提供的信息衍生出的顶视二进制掩码进行比较。性能指标——特别是F1分数和AUC-ROC——分别达到了高达0.95和0.83的数值。结果表明该方法具有稳健性，能够一致地识别出潜在缺陷区域，并且误报极少，漏检情况也很少。自适应频率阈值确保了在处理不同板材时的灵活性，为检测结构异常提供了可扩展框架。此外，由于其通用化的阈值机制，该方法还可以适应其他基于频率的信号，并具有整合多模态传感器融合的潜力。这种自动化和可扩展的工作流减少了人工干预，保证了准确而高效的缺陷检测，进一步推进了无损评估（NDE）技术的发展。
URL
https://arxiv.org/abs/2412.17953
