Prediction and Prevention of Bridge Performance Degradation Due to Corrosion, Material Loss, and Microstructural Changes
Authors: Mekala Bharath Krupa Teja , Frame Lesley 
Published: August 2024

Corrosion can significantly impact the performance of structures, and corrosion-related maintenance is a primary contribution to annual transportation infrastructure costs. The current NBI data reports that the poor condition of the bridges around US have increased by 10%, and 50% of the bridges in Connecticut are either in poor or fair condition. Bridges in the US share approximately 37% of the total annual cost due to corrosion. Bridges are primarily constructed of A7, A36, A588, and A242 steel. ASTM A7 steel was used in bridge construction until 1967. Major limitations of A7 steel include poor corrosion resistance and mechanical properties, which ultimately led to the replacement of A7 steel by A36 steel after 1967. Though A36 steel shows better mechanical properties, it does not possess improved corrosion performance compared to A7. A588 and A242, which are also called weathering steels, display significantly higher corrosion resistance paired with desired mechanical properties, but the cost of these so-called weathering steels is also higher. The present study aims at characterizing the corrosion performance of bridge steels A7, A36 and A588 under a wide range of chloride-rich environments so that we may improve our predictions for bridge longevity.

A major portion of the existing literature on steel corrosion mechanisms considers the study of plain carbon steel. Plain carbon steels form corrosion products such as Goethite, Akageneite, Lepidocrocite, and Magnetite, having the chemical formulas α-FeOOH, β-FeOOH and γ-FeOOH and Fe3O4 respectively. γ-FeOOH forms during the initial stages of corrosion formation where Magnetite being the final product. β-FeOOH is an intermediate product but is detrimental for the performance of the bridge since the oxide formed is not protective.

The addition of specific alloying elements in the bridge steel grades (including Cu) can heavily influence the corrosion mechanisms at work and subsequently the corrosion rates. Further, the exposure to various environmental conditions such as rain, snow, hail, wind, and thermal cycles will lead to successive wetting and drying conditions. Bridges in colder climates are exposed to de-icing salts that can accumulate on horizontal surfaces or within the corrosion products that form. Each of these factors makes accurate prediction of corrosion rates on specific bridge components difficult.

The current study compares the corrosion performance of A7, A36 and A588 alloys at different NaCl concentration exposure (1 wt.%, 2 wt.%, 3.5 wt.% and 5 wt.%) for difference steel surface conditions (oxidized and polished). Corrosion studies on these alloys were performed using wet-dry cyclic exposure to obtain corrosion at an accelerated pace. Each wet-dry cycle consisted of 15 minutes of wetting and 1 hour drying period. Test coupons were in the size of 50 mm×25 mm×4.76 mm. A total of 400 wet/dry cycles were performed to determine the effect of longer exposure to salt environments. Batches of samples of each alloy were collected at 25, 50,75, 100, 150, 200, 250, 300, 350, 400 cycles to observe corrosion trends with increase in cycles. Further, the samples were photographed, and the surfaces were compared. Corrosion samples retrieved from bridges in Connecticut were compared to the lab-tested coupons. The phases present in the corrosion products were characterized using XRD and SEM. The results showed lower magnetite compared to the field samples, and the presence of detrimental β – FeO(OH,Cl) was significantly higher at the higher [Cl-] concentrations. Mass gain studies were performed by measuring the weight of each test coupon at regular intervals throughout the wet-dry cyclic testing. The mass gain per unit surface area of the sample was calculated for the different conditions. Cross-sections of the corroded samples were examined using optical light microscopy and SEM-EDS. These results will be used to build and validate a prediction tool for field evaluation of bridge corrosion.