Structural Novelty Detection With Modified Mel‐Frequency Cepstral Coefficients and Bhattacharyya Distance

A novel data‐driven damage detection framework, based on probabilistic modeling of mel‐frequency cepstral coefficient (MFCC) distributions, is proposed. This study replaces traditional autospectral densities with the power spectrum derived from cross‐spectral density ratios in MFCC extraction, enhancing sensitivity to subtle dynamic changes across structural locations. The Bhattacharyya distance ( D B ) is introduced to quantify dissimilarities between MFCC distributions under baseline and potential damage scenarios. Subsequently, a damage indicator (DI B ) based on the Bhattacharyya distance is proposed. To mitigate uncertainties caused by environmental noise and measurement variability, a statistically sound threshold is established through Bayesian resampling and Monte Carlo simulation. When the DI B values of structural states exceed this threshold, it indicates the presence of damage. Additionally, a vectorization scheme is employed to improve computational efficiency, enabling faster processing of multichannel data. The accuracy and effectiveness of the proposed method are validated through a laboratory experiment involving four beams and a field test conducted on a steel truss bridge. The results demonstrate the proposed method’s ability to detect and classify damage states accurately under diverse conditions, highlighting its applicability for reliable structural health monitoring (SHM).