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Linking NDE Data and Bridge Deck Performance
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2025-02-01
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Edition:Final Report
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Abstract:The researchers and the University of Missouri collaborated to complete a comprehensive analysis of nondestructive evaluation (NDE) data collected from the first phase of accelerated testing performed at the Bridge Evaluation and Accelerated Structural Testing facility at Rutgers University in New Jersey. The research team evaluated the use of data from predictive NDE (PNDE) and defect NDE (DNDE) techniques in bridge deck performance models to offer recommendations for incorporating NDE data to accurately predict the actual performance of the deck specimen subject to accelerated testing. The NDE data reliability analysis for assessing bridge deck conditions incorporated several techniques, including time-lapsed analyses to assess the temporal consistency of data collected over the lifespan of the accelerated testing, chronological comparison studies of PNDE and DNDE techniques throughout the accelerated testing experimentation, and a detailed evaluation of each technology’s effectiveness in predicting bridge conditions through NDE condition indexing and the use of receiver-operator characteristic (ROC) analysis. In addition, the researchers used a mechanistic service life modeling technique to characterize the bridge deck condition throughout the accelerated testing, from original construction to corrosion initiation to visible damage detection. The team used the collected NDE data as model inputs to enhance the model agreement to observed performance. Additionally, the team applied the service life modeling technique to two in-service bridges in Iowa to further explore potential integrations between NDE data and deck performance. Deterioration models developed in consultation with NDE data were also studied and compared with condition indexes, defect indexes, and deck condition ratings to ascertain the quantitative correlations between NDE and deck performance measures. The data from bridge accelerated testing served as a pivotal demonstration in this analysis, illustrating how NDE data integration not only refines and enhances model predictions but also provides a more nuanced understanding of bridge deck health over time.
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[PDF-11.92 MB]
