Probabilistic assessment of time to cracking of concrete cover due to corrosion using semantic segmentation of imaging probe sensor data

Abstract

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.