Classification, Localization, and Quantification of Structural Damage in Concrete Structures using Convolutional Neural Networks

Applications of Machine Learning (ML) algorithms in Structural Health Monitoring (SHM) have recently become of great interest owing to their superior ability to detect damage in engineering structures. ML algorithms used in this domain are classified into two major subfields: vibration-based and image-based SHM. Traditional condition survey techniques based on visual inspection have been the most widely used for monitoring concrete structures in service. Inspectors visually evaluate defects based on experience and engineering judgment. However, this process is subjective, time-consuming, and hampered by difficult access to numerous parts of complex structures. Accordingly, the present study proposes a nearly automated inspection model based on image processing, signal processing, and deep learning for detecting defects and identifying damage locations in typically inaccessible areas of concrete structures. The work conducted in this thesis achieved excellent damage localization and classification performance and could offer a nearly automated inspection platform for the colossal backlog of ageing civil engineering structures