This paper presents a framework for segmentation of imaging probe corrosion sensor data using a deep learning algorithm and estimation of the remaining service life of the structure using the segmented data. The sensor consists of a sacrificial metal foil that is imaged using the optical probe and the changes in the images as corrosion develops can be used as a proxy to monitor the condition of the concrete. In this paper, DeepLabV3+ which is a deep learning network architecture is implemented for the segmentation of sensor images. The neural network model trained on labeled corroded and uncorroded images of foil captured under various chloride levels yields a test accuracy of 95%. The mass loss of steel is estimated using a Bayesian curve fitted over the estimated mass loss from the segmented images and the mass loss from the accelerated corrosion test. This is then used for the estimation of the corrosion rate, which is given as the input for the probabilistic estimation of the time at which the concrete cover is expected to crack. A case study is presented to demonstrate how the segmented images from the neural network model can be used for estimating the time to cracking of concretes.

本文提出了一种框架，用于使用深度学习算法对成像探头腐蚀传感器数据进行分割，并使用分割数据估计结构的剩余使用寿命。该传感器由使用光学探头成像的牺牲金属箔组成，随着腐蚀的发展，图像的变化可用作监测混凝土状况的代理。在本文中，DeepLabV3+ 是一种深度学习网络架构，用于传感器图像的分割。在不同氯化物水平下捕获的箔的标记腐蚀和未腐蚀图像上训练的神经网络模型产生 95% 的测试准确度。钢的质量损失是使用贝叶斯曲线估计的，该曲线拟合来自分段图像的估计质量损失和来自加速腐蚀测试的质量损失。然后将其用于腐蚀速率的估计，腐蚀速率作为混凝土保护层预计开裂时间的概率估计的输入。一个案例研究展示了如何使用神经网络模型的分割图像来估计混凝土开裂的时间。