Near-field Sub-band Beamforming for Damage Detection in Bridges

Traditional structural health monitoring techniques based on the vibration response of bridge structures are limited because of several factors — including a poorly formed aggregate system model, very low SNR, and unrealistic boundary conditions. Many times, these methods rely on global parameters to describe the dynamic behavior of local structural elements. In this paper, we proposed a novel efficient SHM technique that employs the use of compactly supported sub-band space/ frequency and time/frequency analysis using local vibration characteristics. To overcome the problem of the low-error sensitivity of features extracted from vibration signals, a near-field adaptive beamforming approach was used. This technique allows the sensor array to `scan' local portions of the structure, resulting in accurate spatial selectivity on the array and high signal-to-noise ratio for any given scan direction. Moreover, the sensor array was in direct contact with the vibrating structure, and thus the measured source is in the near-field of the array. Therefore, compensation of the sensor output was needed. Sub-band analysis and adaptive beamforming were integrated in a wavelet packet sub-band framework. We utilized the 3D energy map of the optimized sub-band signals for any given scan direction and for each sub-band center frequency as the damage detection feature. We validated our method using classical finite elements approximation to the dynamic behavior of the system. The comparison of a simulated undamaged simply supported beam with a damaged equivalent revealed that damage is localized in frequency and aligned with the direction of the simulated damage. The energy signature comparison after the beamforming stage validates these results, localizing the damage in areas of high probability around the direction of the damage. Automatic analysis of the energy comparison map was possible using a pseudo density estimation of the damage location, allowing for an automatic damage detection procedure. The focus of our research was aimed toward the adaptation of this damage detection method to real highway bridges.