Dynamic Response of an Orthotropic Bridge Deck Subjected to EOVs

Abstract

The Haringvlietbrug, which was delivered in 1964 and connects the island of Goeree-Overflakkee to the Dutch mainland, approaches the end of its lifetime. Rijkswaterstaat and Witteveen+Bos investigate the potential use of vibration-based structural health monitoring (SHM) to track deterioration. In vibration-based SHM, a change in the structure’s eigenfrequencies indicates probable damage but most current approaches focus on fundamental modes of the structure which are not or hardly affected by small structural impairments. In this study, vibration-based SHM was employed on local modes of vibration of the Haringvlietbrug, with the hope of detecting small-scale damage. New obstacles arose as a result of this: the influence of environmental and operational variabilities (EOV) on the eigenfrequencies of the structure was often larger than the influence of small damage. As a result, the EOVs should be filtered out of the recorded acceleration data before damage sensitive features can be extracted.
To investigate this, 32 accelerometers and 16 temperature sensors were installed in two segments of the Haringvlietbrug. The sensors yield a large data set which was analysed extensively. This made it clear, among other things, that the dynamic response of the bridge deck showed large variability when comparing different vehicle passages.

The first research objective was to investigate the applicability of similarity filtering (SF) to filter out operational variabilities from the Haringvlietbrug acceleration signals to extract damage-sensitive features. SF amplified similarities and damped differences between samples of vehicle passages. This way, only consequently excited modes should remain in the signals which were subsequently used as damage-sensitive features for SHM.
This study found that using SF to filter the operational variabilities from the Haringvlietbrug data set was ineffective. Three reasons were identified for this. First, the method was not robust as a single deviating sample or a poorly chosen filter coefficient significantly influenced the results. Secondly, missing closely-spaced modes might have caused inconsistency of the results. Lastly, SF was not able to converge to consistent behaviour as the variability of response of the bridge deck might be too great for different passages. The latter reason was investigated further in the remaining of the research.

The second research objective was to improve the understanding of the effect of vehicles on local bridge vibrations of the Haringvlietbrug for the application of vibration-based SHM. First, the eigensystem realisation algorithm (ERA) was used to identify and compare consistent mode shapes in order to better understand bridge deck vibrations and find patterns in the eigenfrequencies. ERA is an operational modal analysis (OMA) and output-only system identification technique.
ERA was able to identify two consistent modes in the data but they had a large variance in both shape and eigenfrequency. The uncertainties of the results were too large to draw firm conclusions based on ERA because two of ERA’s key assumptions were not perfectly met and the input samples were not optimal.

Next, a semi-analytical model of a segment of the Haringvlietbrug was built to simulate the important sources of the variability as found in the acceleration recordings of the Haringvlietbrug. The goal of the model was to investigate the sensitivity of the response of the beam to variations of input parameters. The model was both used for time-history analyses and eigenfrequency analyses. Parametric studies showed that the response of the beam was highly sensitive to the time delay between moving masses (dependent on the axle configuration and vehicle velocity), the unevenness (describing any source of vibration of the interaction between vehicle and bridge deck) and the vehicle velocity. These parameters influenced both the amplitude and the shape of the acceleration response of the Haringvlietbrug bridge deck in the time and frequency domain.
The effect of the three dominant parameters was further investigated by simulating four characteristic cases from the Haringvlietbrug data set. Hypotheses on the source of the measurement variability were formed by qualitatively comparing the results of the model to the measured response of the bridge. The model parameters were able to describe a large part of the variability but it was concluded that it is likely that some factors that were not included in the model also play a role in the variability of the measurements.

The above findings led to the following recommendations for further research. Firstly, it is recommended to only select similar vehicle passages for SF because this would improve the ability to converge to consistent modal behaviour. Secondly, should someone want to further explore ERA, it is recommended to equip a section of the bridge with a high spatial density of accelerometers to improve the reliability of the results.
Next, some recommendations related to the model were made. The first step of follow-up research would be to verify the influence of the model parameters on the dynamic response of the Haringvlietbrug by experimenting with different test vehicles. Subsequently, depending on the importance of the parameters, possible extensions of the measurement set-up for a new campaign were proposed. Secondly, the current model could be improved by introducing more parameters, like acceleration of the moving mass or by implementing a more realistic vehicle model, to be better able to explain the dynamic variability. Thirdly, more parameters could be investigated by building a 3D finite element model. This would make it possible to investigate the influence of the presence of multiple vehicles on the bridge and 3D wave propagation.
Upcoming research into data-driven approaches of vibration-based SHM of bridge decks is recommended to focus on similar passages as not all samples contain the same information on the dynamic behaviour of the structure. Decreasing the variability of the input samples might improve the performance of the algorithms.