Justin Dahlberg

Impact of Concrete Deck Removal on Horizontal Shear Capacity of Shear Connections

Due to the higher deterioration rate of bridge decks compared to other bridge components, intermittent replacement of bridge decks stands to be a viable approach to extending bridge service life without replacing entire bridge components. A question was raised regarding the necessity of removing all of the concrete during a deck replacement due to a lack of understanding of the effects of the extent and quality of concrete removal on the horizontal shear capacity. The purpose of this study is to investigate the influence of the remaining concrete around the shear connectors on the horizontal shear capacity of the shear connection. An experimental program consisting of push-out testing was implemented. Twenty seven small- scale specimens were fabricated using three different concrete removal levels (i.e., 50%, 75% and 100% concrete removal) and three types of shear connectors (i.e., shear stud, channel and angle-plus-bar). Push-out testing was conducted for all the fabricated specimens until the specimens failed. During testing, the ultimate horizontal shear load of each shear connection and the slip between the concrete deck and the steel girder were recorded. The failure modes of all specimens were also documented. Simplified analysis and finite element (FE) analysis were conducted to assist understanding and interpreting the test results. It was found that the horizontal shear strengths of the shear stud shear connection and the channel shear connection were not sensitive to the quantity of concrete removed.

Use of a Structural Health Monitoring System for the Assessment of Bridge Load Rating

A technical framework that uses a Structural Health Monitoring (SHM) system, which continuously measures bridge response to unknown ambient trucks, was proposed to calculate load ratings based upon finite element model simulations coupled with a statistical backbone. A steel bridge located in Iowa was selected to demonstrate this technical framework. Critical locations of the bridge were instrumented with a network of fiber optic sensors that collect real-time strain data from ambient trucks. As their characteristics were unknown, they were statistically characterized in terms of configuration and weight using Weigh-In-Motion (WIM) data collected in Iowa. Subsets of strain data were randomly selected to optimize computational models created with finite element software. The optimized models were used to determine distributions of load ratings following the AASHTO Load Factor Rating (LFR) methodology. Distributions were created for each strain set. The distributions, which account for variability in unknown trucks, can be used to evaluate the structural capacity of the bridge.