STRUCTURAL PERFORMANCE ASSESSMENT ON PREVENTIVE MAINTENANCE/REHABILITATION OF STEEL GIRDER BRIDGE SYSTEMS

Abstract

Bridge maintenance, including preventive maintenance, rehabilitation, and replacement keeps the structure safe in its service life. Bridge maintenance methods have developed and expanded to bridge inspection, bridge condition assessments, structural health monitoring technologies, service life prediction, and maintenance with new materials or technologies. This dissertation proposes two structural performance assessments, (1) a rapid machine learning assessment classifying whether the design is under an acceptable range; and (2) comparing the structural health monitoring data with engineers’ predictions to evaluate the current structural performance.The first part of this dissertation focuses on preventive maintenance evaluation. This part discusses and plans a specific topic to instrument a newly constructed link slab system on a multiple simply-supported bridge. Before any simulation and field test of the general steel I-girder bridge model was conducted, literature regarding bridge maintenance, performance assessment, the durability of bridges, structural health monitoring method, current condition assessment methods, and research on the material and structural behavior of link slabs were reviewed and investigated. Then comprehensive experimental programs on the new materials HPFRC and ECC were conducted, and the extensive hands-on laboratory results update the nonlinearity and accuracy of the structure model. Moreover, a series of data analyses of the current steel bridge in the United States was conducted for further database establishment. Two sets of simulation-based finite element parametric analyses of the bridge with protective maintenance or structural repairing were introduced to generate preferred designs and further be used for rapid performance assessment. The inputs are the configuration of the bridge and the proposed work or deteriorated location, which generate the dataset for the training, validating, and testing of the evaluation model. The resulting regression model allows for quick assessment of the function developed by taking advantage of machine learning. This research verifies the assessment’s results using the Maryland Transportation Authority (MDTA) pilot HPFRC link slab system on the bridge overpass I-95 as a case study by comparing the prediction and actual structure health monitoring data after the assessment’s results were verified, and its performance was evaluated. This dissertation also examines one case from the Maryland State Highway Administration (MDSHA) bridge over the Patapsco River, which has several frozen expansion rocker bearings. This restrained the longitudinal movement of the superstructures due to thermal expansion and contraction. It has partially recovered to its normal condition after repairing and strengthening the pier under this bearing. In this study, we combined the finite element analysis and monitoring data to simulate structural behavior, and evaluate repair work using the proposed methods. Finally, This research evaluates the repaired bridge with partially strengthened structural components (i.e., deck, girders, or piers) and forecasts its wear and tear. In this study, the original and deteriorated bridges were numerically modeled first and simplified to 3D grid models. Then, the current condition rating process was used to determine the structural performance at the element level. After establishing the evaluation criteria and investigating the corresponding condition ratings, the rapid assessment models for the repaired bridges were carried out. The condition prediction relied on historical ratings and (if any) monitoring data.