abstract: Civil infrastructures are susceptible to damage under the events of natural or manmade disasters. Over the last two decades, the use of emerging engineering materials, such as the fiber-reinforced plastics (FRPs), in structural retrofitting have gained significant popularity. However, due to their inherent brittleness and lack of energy dissipation, undesirable failure modes of the FRP-retrofitted systems, such as sudden laminate fracture and debonding, have been frequently observed. In this light, a Carbon-fiber reinforced Hybrid-polymeric Matrix Composite (or CHMC) was developed to provide a superior, yet affordable, solution for infrastructure damage mitigation and protection. The microstructural and micromechanical characteristics of the CHMC was investigated using scanning electron microscopy (SEM) and nanoindentation technique. The mechanical performance, such as damping, was identified using free and forced vibration tests. A simplified analytical model based on micromechanics was developed to predict the laminate stiffness using the modulus profile tested by the nanoindentation. The prediction results were verified by the flexural modulus calculated from the vibration tests. The feasibility of using CHMC to retrofit damaged structural systems was investigated via a series of structural component level tests. The effectiveness of using CHMC versus conventional carbon-fiber reinforced epoxy (CF/ epoxy) to retrofit notch damaged steel beams were tested. The comparison of the test results indicated the superior deformation capacity of the CHMC retrofitted beams. The full field strain distributions near the critical notch tip region were experimentally determined by the digital imaging correlation (DIC), and the results matched well with the finite element analysis (FEA) results. In the second series of tests, the application of CHMC was expanded to retrofit the full-scale fatigue-damaged concrete-encased steel (or SRC) girders. Similar to the notched steel beam tests, the CHMC retrofitted SRC girders exhibited substantially better post-peak load ductility than that of CF/ epoxy retrofitted girder. Lastly, a quasi-static push over test on the CHMC retrofitted reinforced concrete shear wall further highlighted the CHMC's capability of enhancing the deformation and energy dissipating potential of the damaged civil infrastructure systems. Analytical and numerical models were developed to assist the retrofitting design using the newly developed CHMC material. / Dissertation/Thesis / Ph.D. Civil and Environmental Engineering 2013

Condition assessment of civil engineering infrastructure systems is of growing importance as they face aging and degradation due to both human-made activities and environmental factors. Nevertheless, challenges persist in data collection, leading to “data scarcity”, and the need for frequent site visits in inspections, presenting significant obstacles in the assessment of the civil infrastructure systems. This dissertation aims to overcome these challenges by exploring the potential of two emerging technologies: Generative Artificial Intelligence (AI) and Virtual Reality (VR). In tackling the issue of data scarcity, the research question revolves around how Generative AI can be utilized to mitigate data collection-related constraints and increase data availability, thus facilitating health monitoring applications of infrastructure systems. For that, using various Generative AI models, the dissertation works on acceleration response data generation, including data augmentation and domain translation applications on different structures. In addressing the site visit challenge, the dissertation focuses on the use of VR to bring the infrastructure to the experts in a single collaborative immersive environment and investigate its impact on decision-making in inspections. For that, using VR technology, the dissertation develops a Virtual Meeting Environment (VME) integrated with the infrastructure data and models presented through novel immersive visualization techniques. The dissertation further investigates the impact of VME on decision-making in infrastructure inspections through experimentation with engineers. These investigations of the use of Generative AI and VR demonstrate various contributions. Generative AI effectively tackles the need for vast datasets in data-intensive damage detection applications. It also demonstrates its potential in estimating representative response data for various structural conditions across dissimilar infrastructures. VME, on the other hand, offers an increased understanding of the material along with a safer, practical, and cost-effective complementing alternative to traditional in-person site visits. It further reveals how VME improves decision-making in infrastructure inspections.

Mechanically Stabilized Earth (MSE) walls retain soil on steep, unstable slopes with crest loads. Over the last decade, they are becoming quite popular due to their low cost-to-benefit ratio, design flexibility, and ease of construction. Like any civil infrastructure, MSE walls need to be continuously monitored according to transportation asset management criteria during and after the construction stage to ensure that their expected serviceability measures are met and to detect design and/or construction issues, which could lead to structural failure. Current approaches for monitoring MSE walls are mostly qualitative (e.g., visual inspection or examination). Besides being time consuming, visual inspection might have inconsistencies due to human subjectivity. Other monitoring approaches are based on using total station, geotechnical field instrumentations, and/or Static Terrestrial Laser Scanning (TLS). These instruments are capable of providing highly accurate, reliable performance measures. However, the underlying data acquisition and processing strategies are time-consuming and are not scalable. This research focuses on a comprehensive strategy using a Mobile LiDAR Mapping System (MLS) for the acquisition and processing of point clouds covering the MSE wall. The strategy produces standard serviceability measures, as defined by the American Association of State Highway and Transportation Officials (AASHTO) – e.g., longitudinal and transversal angular distortions. It also delivers a set of recently developed measures (e.g., out-of-plane offsets and 3D position/orientation deviations for individual panels constituting the MSE wall). Moreover, it is also capable of handling MSE walls with smooth or textured panels with the latter being the focus of this research due to its more challenging nature. For this study, an ultra-high-accuracy wheel-based MLS has been developed to efficiently acquire reliable data conducive to the development of the standard and new serviceability measures. To illustrate the feasibility of the proposed acquisition/processing strategy, two case studies in this research have been conducted with the first one focusing on the comparative performance of static and mobile LiDAR in terms of the agreement of the derived serviceability measures. The second case study aims at illustrating the feasibility of the proposed strategy in handling large textured MSE walls. Results from both case studies confirm the potential of using MLS for efficient, economic, and reliable monitoring of MSE walls.