The application of deep learning (DL) algorithms has become of great interest in recent years due to their superior performance in structural damage identification, including the detection of corrosion. There has been growing interest in the application of convolutional neural networks (CNNs) for corrosion detection and classification. However, current approaches primarily involve detecting corrosion within bounding boxes, lacking the segmentation of corrosion with irregular boundary shapes. As a result, it becomes challenging to quantify corrosion areas and severity, which is crucial for engineers to rate the condition of structural elements and assess the performance of infrastructures. Furthermore, training an efficient deep learning model requires a large number of corrosion images and the manual labeling of every single image. This process can be tedious and labor-intensive. In this project, an open-source steel bridge corrosion dataset along with corresponding annotations was generated. This database contains 514 images with various corrosion severity levels, gathered from a variety of steel bridges. A pixel-level annotation was performed according to the Bridge Inspectors Reference Manual (BIRM) and the American Association of State Highway and Transportation Officials (AASHTO) regulations for corrosion condition rating (defect #1000). Two state-of-the-art semantic segmentation algorithms, Mask RCNN and YOLOv8, were trained and validated on the dataset. These trained models were then tested on a set of test images and the results were compared. The trained Mask RCNN and YOLOv8 models demonstrated satisfactory performance in segmenting and rating corrosion, making them suitable for practical applications.

Uncrewed Aerial Vehicles (UAV) constitute a rapidly evolving technology field that is becoming more accessible and capable of supplementing, expanding, and even replacing some traditionally manual bridge inspections. Given the classification of the bridge inspection types as initial, routine, in-depth, damage, special, and fracture critical members, specific UAV mission requirements can be developed, and their suitability for UAV application examined. Results of a review of 23 applications of UAVs in bridge inspections indicate that mission sensor and payload needs dictate the UAV configuration and size, resulting in quadcopter configurations being most suitable for visual camera inspections (43% of visual inspections use quadcopters), and hexa- and octocopter configurations being more suitable for higher payload hyperspectral, multispectral, and Light Detection and Ranging (LiDAR) inspections (13%). In addition, the number of motors and size of the aircraft are the primary drivers in the cost of the vehicle. 75% of vehicles rely on GPS for navigation, and none of them are capable of contact inspections. Factors that limit the use of UAVs in bridge inspections include the UAV endurance, the capability of navigation in GPS deprived environments, the stability in confined spaces in close proximity to structural elements, and the cost. Current research trends in UAV technologies address some of these limitations, such as obstacle detection and avoidance methods, autonomous flight path planning and optimization, and UAV hardware optimization for specific mission requirements.

Cellulose nanofibrils (CNFs) have the potential to enhance cement and concrete properties because of the way they change both how water is distributed during mixing and how they modify the hydration process. In this work, the effects of CNFs on certain properties of cement paste and concrete were investigated. For the cement paste, workability, shrinkage properties, and compressive strength were investigated. In the cement paste study, 32 batches with variable CNF concentrations in 4 groups with different water-to-cement (w/c) ratios (0.35, 0.40, 0.45, and 0.50) were prepared and tested. Two rheological tests were performed; one used an ammeter to measure torque versus rotational speed, and a second used a standard flow table. Both tests showed a decrease in the workability of cement pastes that was due to the increase in CNF volume for all pastes, which suggests that CNF retains mix water in the fresh state. Thus, the water held in the CNF is not available during initial mixing of the cement paste. The results of the free shrinkage tests for all 32 cement pastes were collected for up to 90 days. Additional results showed that at a low w/c ratio (0.35), adding a small quantity of CNF (0.05 %) can reduce free shrinkage by 13 %. In compressive strength tests, a small dosage of CNF (0.05 %) improved compressive strength (up to 28 %), but higher dosages reduced strength. Results suggest that CNF has a good potential to be considered as a new natural plant-based internal curing agent in cement paste and concrete. For the concrete specimens, the primary focus was on workability effects and compressive strength. In the concrete study, twelve batches with different CNF volumes were prepared, and the corresponding slump results were measured. Results showed that to preserve the slump values, extra water content of 5–8 % should be added for every 0.1 % of CNF incorporated in the concrete mixture. Based on compressive strength tests, it has been shown that CNF is the weakest link in concrete microstructures and controls the compressive strength.