After detecting initial delamination damage in a hotspot region of a composite structure monitored through a data-driven approach, the user needs to decide if there is an imminent structural failure or if the system can be kept in operation under monitoring to track the damage progression and its impact on the structural safety condition. Therefore, this study proposes delamination area quantification by stochastically interpolating global damage indices based on Gaussian process regression and taking into account uncertainty. Auto-regressive models are applied to extract damage-sensitive features from Lamb wave signals, and the Mahalanobis squared distance is used to compute damage indices. Two sets of laboratory tests are used to demonstrate the effectiveness of this methodology—one in carbon–epoxy laminate with simulated damage under temperature changes to show the general steps of the procedure, and a second test involving a set of carbon fiber–reinforced polymer coupons with actual delamination caused by repeated fatigue loads. Various levels of progression damage, measured by the covered area of delamination, are monitored using piezoelectric lead zirconate titanate patches bonded to the structural surfaces of these setups. The Gaussian process regression proved to be capable of accommodating the uncertainties to relate the damage indices versus the damaged area. The results exhibit a smooth and adequate prediction of the damaged area for both simulated damage and actual delamination.

在通过数据驱动方法检测到的复合结构的热点区域中检测到初始分层损坏后，用户需要确定是否即将发生结构故障，或者是否可以在监视下继续运行系统以跟踪损坏的进展和它对结构安全状况的影响。因此，本研究提出了基于高斯过程回归并考虑不确定性的随机插值整体破坏指数的分层区域量化方法。应用自回归模型从Lamb波信号中提取对损伤敏感的特征，并且使用Mahalanobis平方距离来计算损伤指数。两组实验室测试用于证明此方法的有效性-一组在碳-环氧层压板中，在温度变化下模拟损坏以显示该方法的一般步骤，第二组测试涉及一组碳纤维增强的聚合物试样由于反复的疲劳载荷而导致实际分层。使用分层的压电锆钛酸铅贴片将这些分层破坏的程度（通过分层的覆盖面积测量）进行监测，这些压电钛酸锆钛酸铅贴片固定在这些装置的结构表面上。高斯过程回归证明能够适应不确定性，将损伤指数与损伤面积联系起来。结果表明，对于模拟损坏和实际分层，可以对损坏区域进行平滑而充分的预测。