AbstractStructural health monitoring (SHM) enables bridge owners to evaluate bridge conditions efficiently and accurately. When implementing SHM, a popular method to detect the abnormal response of a structure is statistical pattern recognition. This often involves unsupervised statistical analysis due to a lack of measured SHM data from abnormal conditions. In this study, a novel methodology to calculate strain threshold indices (STIs) and establish decision boundaries (DBs) was used to detect the abnormal responses of the Indian River Inlet Bridge (IRIB), Sussex County, Delaware. First, a series of statistical models were applied and compared. Gaussian three mixture distributions were the optimal statistical models for the heavy vehicle-induced strain peaks. Threshold values for the strain gauges were selected using 99% uppers limits (USLs), and these limits were used to detect the abnormal response of the IRIB. The outlier ratios (R) for the sensors were calculated based on the threshold values. Corresponding STIs were defined by analyzing R, and DBs were determined using a t-distribution. The abnormal responses of the IRIB were detected by comparing the STI and DBs. The validity and sensitivity of the proposed methodology were demonstrated through simulated data that was created by perturbing the actual collected SHM data. Varying degrees of simulated damage were successfully detected using the proposed DBs. The proposed methodology showed promise when short and long-term abnormal responses and could provide practical guidance for bridge owners when using SHM data in their decision-making process.