Oluropo Ogundipe

GPS monitoring of a steel box girder viaduct

Structural performance monitoring of bridges has increased as major infrastructure ages and is required to sustain loads that are significantly greater than those predicted during design. Structural stiffness and/or mass distribution can change over the lifespan of a bridge structure. Resulting changes in profile or resonant frequency provide key indicators of change, and may identify structural defects. Field tests using GPS for monitoring relatively small deformations were carried out on a steel box girder viaduct bridge in the UK. The configuration consisted of five GPS receivers located at key locations on the viaduct and two reference GPS receivers. GPS data was collected at either 10 Hz or 20 Hz and post-processed using proprietary software, along with appropriate filtering and spectral analysis. Three main frequencies were clearly detected by the GPS in the vertical component. A previously reported frequency of approximately 0.56 Hz was identified along with two other frequencies. The peak vertical deflections lie in the range of ± 50 mm, while lateral and longitudinal deflections of much smaller magnitude – in the order of a few mm – are also measured. The use of GPS leads to readily obtained and useful engineering data for continued monitoring of structures.

Wavelet De-noising of GNSS Based Bridge Health Monitoring Data

Abstract GNSS signal multipath occurs when the GNSS signal reflects of objects in the antenna environment and arrives at the antenna via multiple paths. A bridge environment is one that is prone to multipath with the bridge structure, as well as passing vehicles providing static and dynamic sources of multipath. In this paper, the Wavelet Transform (WT) is applied to bridge data collected on the Machang cable stayed bridge in Korea. The WT algorithm was applied to the GNSS derived bridge defection data at the mid-span. Up to 41% improvement in RMS was observed afterwavelet shrinkage de-noisingwas applied.Application of this algorithm to the torsion data showed significant improvement with the residual average and RMS decreased by 40% and 45% respectively. This method enabled the generation of more accurate information for bridge health monitoring systems in terms of the analysis of frequency, mode shape and three dimensional defections.