This paper presents initial findings from the Bridge Evaluation and Accelerated Structural Testing (BEAST) lab at Rutgers University’s Center for Advanced Infrastructure and Transportation (CAIT). The BEAST lab is an accelerated testing facility for full-scale bridge systems. A steel-concrete composite bridge span underwent accelerated degradation, simulating cyclic multi-axle vehicle loads and extreme climatic conditions, particularly freeze-thaw cycles with de-icing salts, replicating 15–20 years of wear in just a few months. Over approximately two years of continuous monitoring, the test aimed to detect structural degradation by employing an extensive sensor network (200+ sensors) covering the deck, girders, joints, and pedestals. This study analyzes trends in sensor data related to structural deterioration during testing, evaluating response variability based on time, temperature, and cumulative load cycles. Non-parametric statistical tests (Mann-Kendall, Pettitt) were applied to detect trends, and linear regression models were developed to quantify the influence of each regressor. Additionally, the sensitivity of performance metrics to sensor positions and alignments was examined.
Morgese et al. (2026) studied this question.