To maintain infrastructure safety and integrity, nondestructive evaluation (NDE) technologies are often used for detection of subsurface defects and for holistic condition assessment of structures. While the rapid advances in data collection and the diversity of available sensing technologies provide new opportunities, the ability to efficiently process data and combine heterogeneous data sources to make robust decisions remains a challenge. Heterogeneous NDE measurements often conflict with one another and methods to visualize integrated results are usually developed ad hoc. In this work, a framework is presented to support fusion of multiple NDE techniques in order to improve both detection and quantification accuracy while also improving the visualization of NDE results. For data sources with waveform representations, the discrete wavelet transform (DWT) is used to extract salient features and facilitate fusion with scalar-valued NDE measurements. The description of a signal in terms of its salient features using a wavelet transform allows for capturing the significance of the original data, while suppressing measurement noise. The complete set of measurements is then fused using nonparametric machine learning so as to relax the need for Bayesian assumptions regarding statistical distributions. A novel visualization schema based on classifier confidence intervals is then employed to support holistic visualization and decision making. To validate the capabilities of the proposed methodology, an experimental prototype system was created and tested from NDE measurements of laboratory-scale bridge decks at Turner-Fairbank highway research center (TFHRC). The laboratory decks exhibit various types of artificial defects and several non-destructive tests were previously carried out by research center technicians to characterize the existing damages. The results suggest that the chosen feature extraction process, in this case the DWT, plays a critical role in classifier performance. The experimental evaluation also indicates a need for nonlinear machine learning algorithms for optimal fusion performance. In particular, support vector machines provided the most robust and consistent data fusion and defect detection capabilities. Overall, data fusion combinations are shown to provide more accurate and consistent detection results when compared to single NDE detection approaches, particularly for the detection of subsurface delamination.

为了维护基础设施的安全性和完整性，非破坏性评估（NDE）技术通常用于检测地下缺陷和对结构进行整体状态评估。尽管数据收集的飞速发展和可用传感技术的多样性提供了新的机遇，但有效处理数据并结合异构数据源以做出可靠决策的能力仍然是一个挑战。异类NDE的测量经常相互冲突，通常会临时开发可视化综合结果的方法。在这项工作中，提出了一个框架来支持多种NDE技术的融合，以提高检测和定量准确性，同时也改善NDE结果的可视化。对于具有波形表示的数据源，离散小波变换（DWT）用于提取显着特征并促进与标量值NDE测量的融合。使用小波变换对信号的显着特征进行描述，可以捕获原始数据的重要性，同时抑制测量噪声。然后使用非参数机器学习融合完整的测量值集，以放宽对有关统计分布的贝叶斯假设的需求。然后，采用基于分类器置信区间的新颖可视化方案来支持整体可视化和决策。为了验证所提出方法的功能，在特纳-费尔班克高速公路研究中心（TFHRC）通过实验室规模的桥面NDE测量创建了实验原型系统并进行了测试。实验室甲板上表现出各种类型的人为缺陷，研究中心技术人员此前曾进行过几次非破坏性测试，以表征现有损坏。结果表明，所选特征提取过程（在本例中为DWT）在分类器性能中起关键作用。实验评估还表明需要非线性机器学习算法以实现最佳融合性能。特别是，支持向量机提供了最强大且最一致的数据融合和缺陷检测功能。总的来说，与单个NDE检测方法相比，数据融合组合可提供更准确和一致的检测结果，尤其是对于地下分层的检测。