The California Department of Transportation (Caltrans) manages hundreds of Box-Beam Over-head Sign Structures (BBOSS) installed across its highway network. These steel structures are particularly vulnerable to corrosion damage, especially at the connections involving ribbed sheet steel vertical diaphragms. Corrosion prevention and repair constitute a significant portion of main-tenance costs for this infrastructure. Structural integrity is currently assessed primarily through visual inspections, a process that is inherently subjective and prone to inconsistencies.
This project addresses the limitation of visual inspection by quantifying the relationship between observed corrosion and the corresponding degradation in structural strength. The overarching goal is to establish a robust and reliable mapping between corrosion indicators and capacity loss. To this end, the research integrates four complementary approaches: (1) highfidelity Finite Element (FE) modeling with model updating through optimization techniques, (2)
full-scale experimental testing of actual BBOSS, (3) field testing involving vibration-based system identification of BBOSS, and (4) Artificial Intelligence (AI)-driven, vision-based methods for automated corrosion detection using image data.
The study focuses on real-world BBOSS installations in California, including one near Davis (Northern California) and another near Seacliff in Ventura County (Southern California). The Seacliff structure, decommissioned due to aging, was transported to the Pacific Earthquake Engineering Research (PEER) Center’s Structural Laboratory for comprehensive testing. In the final phase, comparative testing between the corroded Seacliff structure and an identical, corrosion-free counterpart, supported by updated simulations, was conducted to derive quantitative correlations between corrosion severity and structural capacity loss.
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