18 / 2025-04-24 11:27:19

Natural frequency identification for bridge pier scours assessment using Random Decrement technique and Bayesian FFT

Scour remains a significant threat to bridge infrastructure, as it erodes the sediment around pier
foundations and undermines structural integrity. Conventional inspection techniques, such as
visual surveys and impact vibration tests, are often limited, particularly during flood events
where access constraints impede the timely acquisition of scour-related information. Scour
assessment by identifying the rocking motion natural frequency of the pier through ambient
vibration analysis is expected to address these issues enabling real-time assessment of natural
frequency changes without disrupting bridge traffic or requiring physical inspection, while the
accuracy is limited. The identified natural frequency over months (see Figure 1) shows
significant variation before, during, and after the anti-scouring maintenance work, confirming the
sensitivity of natural frequencies to structural changes. However, the red circles highlight
instances where non-rocking motion modes were unintentionally identified, an issue this study
seeks to minimize. This research thus aims to improve the accuracy of natural frequency
identification by employing advanced modal identification techniques.
The proposed methodology applies the Random Decrement Technique (RDT) to extract free
vibration responses from ambient vibration data, filtering out random noise while taking into
account specific amplitude conditions in sample picking. The specific amplitude conditions were
investigated by examining the clarity of the Power Spectral Density (PSD). PSD analysis of
ambient acceleration measured at a bridge pier demonstrates distinct frequency peaks between
12-14 Hz, consistent with impact test results, for data obtained at certain condition datasets (see
Figure 1a) while other datasets highlight the need for improved frequency identification (see
Figure 1b), where vibration modes, other than the rocking mode, show their distinct peaks. The
sample picking considering amplitude conditions is meant to ensure that rocking-motion
structural vibrations are well excited, improving the accuracy of natural frequency identification.
Fast Bayesian FFT is then used to estimate natural frequencies probabilistically, accounting for
uncertainties in noisy data environments. Together, these methods improve frequency
identification accuracy.
This research highlights the potential of ambient vibration-based monitoring for continuous, non
invasive scour detection, offering a safer and more efficient alternative to traditional methods.
By improving the accuracy of natural frequency identification, this approach can significantly
enhance early detection of scour-induced damage, ultimately improving the resilience and safety
of bridge infrastructure.