Case Studies in Construction Materials (Jul 2024)
Deep learning-based extraction and quantification of features in XCT images of steel corrosion in concrete
Abstract
The corrosion of rebars in concrete has been regarded as one of the most predominant factors for the degradation of reinforced concrete (RC) structures. The accurate predictions of corrosion damages are the basis for the safety assessment of in-service RC structures and the enhancement of new structure designs. X-ray computed tomography (XCT), known for its non-destructive capabilities, has been widely utilized to understand the corrosion process inside the concrete, offering three-dimensional (3D) visualization and aiding in the identification of the parameters in the predictive model of steel corrosion. This study uses U-Net, a deep learning network, to detect various phases of steel corrosion evolution in concrete, including the identification of rebar, corrosion products, pores, cement mortar, and corrosion-induced cracking. A galvanostatic accelerated corrosion test was conducted to simulate rebar corrosion within the mortar. The XCT test was performed, producing a series of XCT images that revealed the corrosion-induced cracks in the concrete. This set of images formed a comprehensive training database for the U-Net model. The performance of the model was significantly improved by data augmentation within the training dataset, thereby enabling it to recognize corrosion-induced cracking and pores. The U-Net model proved effective in accurately segmenting the XCT images into different phases, demonstrating a high accuracy rate of 99.18% on the validation dataset. A mean Intersection over Union (IoU) of 82.1% and a mean F1 score of 89.1% are observed on the test dataset, showing superior segmentation performance on XCT images. This significant advancement has automated the process of visualizing and 3D reconstructing the steel corrosion process inside the concrete. Concurrently, it has enabled the quantification of the volumetric expansion coefficient of corrosion products. The findings reveal spatial and temporal variations in this coefficient due to the migration of the corrosion product through the cracks.
