Vibration-based Structural Health Monitoring (SHM) techniques are effective for identifying global dynamic behavior, but local damage detection and source localization remain challenging, particularly when damage initiates at small scales or in confined joint regions. In steel structures, critical deterioration mechanisms often develop at welded or mechanically connected details, where modal vibration signatures can remain largely unaltered during early damage stages, reducing their diagnostic sensitivity. This limitation motivates the integration of monitoring approaches that are driven directly by material-level response.
Acoustic Emissions (AE) provide a non-destructive and high-sensitivity monitoring strategy generated by transient elastic waves released from a material when mechanically stressed. AE events originate at the damage source itself, enabling a direct assessment of local condition variations in structural details and joints. AE monitoring is therefore particularly suited for steel connections, where deterioration may remain undetected by global dynamic metrics.
Current AE monitoring approaches often rely on elementary signal descriptors—including peak amplitude and waveform duration—which are widely reported in the literature as indicators of critical structural states. On the other hand, dominant frequency tracking can be ambiguous to extract reliably due to noise sensitivity, mode mixing, and signal non-stationarity. To address this challenge, the present work explores the use of Cepstral Coefficients (CC) computed from AE waveforms as a stable and straightforward feature for signal characterization. Unlike spectral peaks, CC are directly and uniquely derived from the input signal through a computationally lightweight procedure. These characteristics make CC attractive for rapid laboratory assessment and future SHM integration.
In this context, an experimental study was carried out on welded, full-penetration steel joint specimens tested at University of Trento. The specimens, instrumented with AE sensors, were tested at the University of Trento under controlled low-cycle loading protocols inducing yielding damage. Based on the controlled load history, a comprehensive dataset of AE signals was collected from both undamaged and damaged specimens to assess how local deterioration alters CC features, which were extracted from raw AE waveforms and statistically compared between undamaged and damaged conditions. The analysis highlights differences in CC distributions, showing potential in discriminating between distinct structural damage states in steel joints without requiring complex frequency identification. The results support the CC-based characterization of AE signals as a complementary monitoring layer for SHM systems targeting critical steel joint regions.
R. Andreotti, A. Bonelli, R. di Filippo et al.· e-Journal of Nondestructive...· 0 citations
Vibrational monitoring of structures traditionally relies on contact sensors such as accelerometers, displacement transducers, and strain gauges, which provide reliable physical information for structural health assessment. However, these sensors require manual installation and direct access to the structure, resulting in practical limitations in terms of installation time, safety, and accessibility, while long-term maintenance may compromise the overall Structural Health Monitoring (SHM) system reliability. These constraints often hinder the systematic implementation of SHM, particularly for large-scale infrastructures such as bridges. Alternative approaches present complementary limitations: vision-based techniques, such as digital image correlation, are sensitive to lighting conditions, camera calibration, and line-of-sight occlusions, whereas fibre-optic sensing systems, despite their high accuracy and distributed capabilities, require permanent installation and physical integration within the structure, limiting their suitability for rapid or temporary monitoring campaigns.
To address these challenges, Ground-Based Interferometric RADAR (GB-InRA) technology has emerged as a promising non-contact alternative for vibration monitoring, enabling safe measurements where visual inspection or contact sensor deployment are impractical. Interferometric radar enables the detection of sub-millimetric displacements by measuring the phase difference between transmitted and received signals. In this context, Real Aperture Radar (RAR), which provides one-dimensional line-of-sight measurements, is typically preferred for vibration monitoring over Synthetic Aperture Radar (SAR), which enables two-dimensional imaging. Nevertheless, radar-based measurements remain sensitive to instrument-to-target distance and antenna tilt, which must be carefully calibrated to ensure reliable results.
In the perspective of bridge monitoring, this work presents an experimental study conducted on a simply supported flexible steel beam (Figure 1). Vibrations were measured using an IBIS-FS microwave interferometric radar and compared against conventional displacement transducers adopted as ground truth. Three corner reflectors were installed at distinct beam locations to enable the estimation of the first three vibration mode frequencies and shapes. Impulsive hammer excitations induced sub-millimetric displacements, allowing a quantitative assessment of radar sensitivity and measurement accuracy. Preliminary results indicate displacement amplitude errors ranging between 0.02 mm and 0.2 mm for reference amplitudes between 0.5 mm and 12 mm, when compared to displacement transducers. Different configurations of vertical tilt and radar-to-target distance were also investigated to evaluate their influence on displacement estimation and modal identification. Overall, the findings demonstrate the effectiveness and practical applicability of GB-InRA as a robust, non-invasive tool for vibration monitoring and modal identification of civil structures.
Chiara Suppi, R. Andreotti, Vikram Kumar et al.· e-Journal of Nondestructive...· 0 citations
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