Reinforced concrete structures degrade due to rebar corrosion, leading to the loss of steel rebar volume. Nondestructive techniques (NDT) are employed for knowing the current state of a corroding structure. However, there are uncertainties in the reported values because of measurement error and noise. In this paper, probabilistic measurement error models (MEM) – relating the “true” value to the NDT reported value – are used, and instrument calibration is proposed to quantify the systematic error and random error. Additionally, there is uncertainty in choosing an appropriate MEM from an ensemble of possible models. This entire process is proposed here within a Bayesian framework of uncertainty quantification. A hyper-robust calibration approach is adopted to avoid the calibrated measurement’s sensitivity to the uncertainties in MEM parameters and in the model selection, such that the actual estimate of the corrosion rate is quantified in the form of a probability density function. The proposed procedure is demonstrated for a linear polarisation resistance based instrument. The proposed approach is found to be suitable for the study case and also general enough to be applied to other NDT instruments.

钢筋混凝土结构由于钢筋腐蚀而退化，从而导致钢筋体积损失。采用非破坏性技术（NDT）来了解腐蚀结构的当前状态。但是，由于测量误差和噪声，报告值存在不确定性。在本文中，使用了将“真实”值与NDT报告值相关联的概率测量误差模型（MEM），并提出了仪器校准来量化系统误差和随机误差。另外，从一组可能的模型中选择合适的MEM存在不确定性。本文在不确定性量化的贝叶斯框架内提出了整个过程。采用一种超鲁棒的校准方法，以避免校准后的测量对MEM参数和模型选择中的不确定性敏感，从而以概率密度函数的形式对腐蚀速率的实际估计值进行量化。针对基于线性极化电阻的仪器演示了所建议的过程。发现所提出的方法适合于研究案例，并且足够通用，可以应用于其他无损检测仪器。