The primary objective of inspecting concrete civil structures is to gather information concerning the deterioration of concrete elements, including issues like concrete cover cracking, delamination, or corrosion. Typically, this data is documented through field inspection notes, hand-drawn sketches, and photographs. Unfortunately, this information is often stored in diverse formats, relying on close-range images and paper-based records. Moreover, the process heavily depends on the inspector's experience, structural knowledge, and familiarity with the material properties of the system under investigation. Traditional inspection methods have inherent limitations, as they generally focus on easily accessible areas due to time constraints, safety concerns, or the challenging terrains often encountered in the field. This is particularly relevant when inspecting large structures like bridges, where examining the entire area would be time-consuming and potentially unsafe. The transfer of knowledge from one inspection period to the next becomes problematic when different inspectors are involved. Hence, there is a compelling need to explore modern inspection and monitoring techniques for structures, with a focus on reducing disruption and enhancing the efficiency and reliability of data acquisition.

In this context, the previously published licentiate thesis was aimed to contribute by developing optical alternatives that complement existing techniques. These alternatives should be cost-effective, suitable for on-site application, and easily deployable. To align with the objectives of this research project, the following research questions were addressed before in the licentiate seminar:

1.    How accurate is Close-Range Photogrammetry (CRP) for monitoring geometric deviations and detecting defects?

2.    In pursuit of maximum accuracy and minimal computation time in crack detection, which convolutional neural network (CNN) architecture performs best in classification and semantic segmentation tasks?

3.    Is there a correlation between surface deformations in reinforced concrete, measured through Digital Image Correlation (DIC), and strains in the embedded reinforcement?

However, there are still challenges to be addressed. Concrete civil structure inspection involves more than just defect detection and measurement. In this final thesis, the objective is to discuss an algorithm for creating an intelligent machine capable of classifying concrete defects. This requires the computer, acting as the inspector, to possess substantial knowledge about the concrete structure, including protocols, standards, guidelines, and an understanding of the overall structure's status. Consequently, two additional research questions are introduced:

1.    Building on our previous discussion in the licentiate seminar regarding the correlation between surface deformations and strains in embedded rebars, we aim to enhance the accuracy of the strain estimation. To achieve this, we intend to train intelligent regression models using available experimental data and newly generated synthetic data. Research Question 1: How can we estimate strains on embedded rebars with the application of machine learning regression, employing a hybrid-learning approach? This question is explored in the paper "Prediction of strain in embedded rebars for RC member: application of a hybrid learning approach."

2.    While computer vision techniques are effective in defect detection, structural health assessment encompasses more than just identifying defects. The objective is to provide a comprehensive solution that bridges the gap between defect detection, classification, and assessment, ultimately contributing to a more accurate understanding of detected defects. Research Question 2: How can we bridge the gap between defect detection and classification to achieve effective defect classification? This question is the subject of the forthcoming manuscript, "Defect Classification and Structural Assessment of Concrete Bridges: A Data-Driven Decision-Making Approach".