Bridges are critical components of highway networks, which provide mobility and economical vitality to a nation. Ensuring the safety and regular operation as well as accurate structural assessment of bridges is essential. Structural Identification (St-Id) can be utilized for better assessment of structures by integrating experimental and analytical technologies in support of decision-making. St-Id is defined as creating parametric or nonparametric models to characterize structural behavior based on structural health monitoring (SHM) data. In a recent study by the ASCE St-Id Committee, St-Id framework is given in six steps, including modeling, experimentation and ultimately decision making for estimating the performance and vulnerability of structural systems reliably through the improved simulations using monitoring data. In some St-Id applications, there can be challenges and considerations related to this six-step framework. For instance not all of the steps can be employed; thereby a subset of the six steps can be adapted for some cases based on the various limitations. In addition, each step has its own characteristics, challenges, and uncertainties due to the considerations such as time varying nature of civil structures, modeling and measurements. It is often discussed that even a calibrated model has limitations in fully representing an existing structure; therefore, a family of models may be well suited to represent the structure’s response and performance in a probabilistic manner. The principle objective of this dissertation is to investigate nonparametric and parametric St-Id approaches by considering uncertainties coming from different sources to better assess the structural condition for decision making. In the first part of the dissertation, a nonparametric StId approach is employed without the use of an analytical model. The new methodology, which is iv successfully demonstrated on both lab and real-life structures, can identify and locate the damage by tracking correlation coefficients between strain time histories and can locate the damage from the generated correlation matrices of different strain time histories. This methodology is found to be load independent, computationally efficient, easy to use, especially for handling large amounts of monitoring data, and capable of identifying the effectiveness of the maintenance. In the second part, a parametric St-Id approach is introduced by developing a family of models using Monte Carlo simulations and finite element analyses to explore the uncertainty effects on performance predictions in terms of load rating and structural reliability. The family of models is developed from a parent model, which is calibrated using monitoring data. In this dissertation, the calibration is carried out using artificial neural networks (ANNs) and the approach and results are demonstrated on a laboratory structure and a real-life movable bridge, where predictive analyses are carried out for performance decrease due to deterioration, damage, and traffic increase over time. In addition, a long-span bridge is investigated using the same approach when the bridge is retrofitted. The family of models for these structures is employed to determine the component and system reliability, as well as the load rating, with a distribution that incorporates various uncertainties that were defined and characterized. It is observed that the uncertainties play a considerable role even when compared to calibrated model-based predictions for reliability and load rating, especially when the structure is complex, deteriorated and aged, and subjected to variable environmental and operational conditions. It is recommended that a family-of-models approach is suitable for structures that have less redundancy, high operational importance, are deteriorated, and are performing under close capacity and demand levels

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Adjacent precast, prestressed box beam bridges have a history of poor performance and have been observed to exhibit common types of deterioration including longitudinal cracking, concrete spalling, and deterioration of the concrete top flange. The nature of these types of deterioration leads to uncertainty of the extent and effect of deterioration on structural behavior. Due to limitations in previous research and understanding of the strength of deteriorated box beam bridges, conservative assumptions are being made for the assessment and load rating of these bridges. Furthermore, the design of new box beam bridges, which can offer an efficient and economical solution, is often discouraged due to poor past performance. Therefore, the objective of this research is to develop improved recommendations for the inspection, load rating, and design of adjacent box beam bridges. Through a series of bridge inspections, deteriorated box beams were identified and acquired for experimental testing. The extent of corrosion was determined through visual inspection, non-destructive evaluation, and destructive evaluation. Non-destructive tests (NDT) included the use of connectionless electrical pulse response analysis (CEPRA), ground penetrating radar (GPR), and half-cell potentials. The deteriorated capacity was determined through structural testing, and an analysis procedure was developed to estimate deteriorated behavior. A rehabilitation procedure was also developed to restore load transfer of adjacent beams in cases where shear key failures are suspected. Based on the understanding of deterioration developed through study of deteriorated adjacent box beam bridges, improved inspection and load rating procedure are provided along with design recommendations for the next generation of box beam bridges.