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Extracting reliable damage-sensitive features from sparse sensor networks under Environmental and Operational Variations (EOV) remains a critical challenge in Structural Health Monitoring (SHM). The purpose of this study is to overcome this limitation by proposing a novel, data-driven framework utilizing a cost-effective network of high-sensitivity triaxial roving accelerometers. The methodology integrates an AutoRegressive with eXogenous inputs (ARX) model and Wavelet Packet Decomposition (WPD) to extract robust, damage-sensitive features from complex vibration data. To handle the high-dimensionality of the extracted signals and achieve optimal multi-sensor data fusion, Block-wise Principal Component Analysis (PCA) is employed as a signal sanitation and feature reduction tool. This algorithmic pipeline is applied to a full-scale bridge pier subjected to RC jacketing. The structural enhancements and dynamic behavior shifts post-retrofitting were statistically quantified using the Mahala Nobis distance. The analysis revealed a 41.2% attenuation in median vibration intensity and successfully verified the structural improvements at a 99% confidence interval, clearly distinguishing the retrofitting effects from ambient noise. The proposed framework successfully isolates true structural changes from EOV, providing a reliable non-destructive evaluation tool for continuous monitoring in practical civil engineering applications.
Dar et al. (Fri,) studied this question.