IRISE Report: Improving Bridge Assessment

(b). Output Image Figure 13. Output example of proposed work 3.1.2 Subsurface Defects Detection Subsurface defects typically result in corrosion-induced deterioration of reinforcement. Its continuous development can cause the loss of structural integrity [25]. Therefore, delamination is one of the most critical defects assessed during the bridge deck inspections. Traditional delamination detection methods, such as hammer sounding and chain dragging, are laborintensive, time consuming, and require maintenance and protection of traffic (MPT). Infrared Thermography (IR) has been developed to detect delamination. It provides fast and effective inspections with reasonable accuracy [26] [27] [28]. The internal delamination acts like an insulating layer that causes different heating and cooling rates from the surrounding concrete.

The thermal contrast in infrared images (IR) can be identified as delamination [29]. This section proposed a semantic image segmentation using a novel deep learning architecture to detect the location of subsurface defects and quantify the defected areas from IR images. The proposed method associates each pixel in infrared images with a class label (defected or sound), in which defected areas are segmented from the sound concrete areas and thus can be localized and calculated. To achieve this goal, a pixel-level separation is required. Semantic pixel-wise segmentation is a state-of-art technique that has been used in many studies [30] [31] for image segmentation. Since the complexity of infrared images is simple, semantic pixel-wise segmentation is promising to result in acceptable outcomes. Deep learning network structures Xception backboned DeepLabV3+ is selected to be used in this study. In the following section, details of the proposed method are provided.