A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Statistical Bridge Signatures
Instrumentation of bridge structures provides a stream of data representing operational structural response under loading. The authors define the term bridge signature as the expected response of a particular bridge under loading, as measured by different instruments. In this research, the authors propose a new method to develop and evaluate a bridge signature. The signature can be monitored over time and statistically evaluated to detect potential structural deterioration and damage. An instrumentation system was implemented on the Powder Mill Bridge in Barre, Massachusetts, as a research prototype for the development of a structural health monitoring (SHM) system. Heavy truck events due to daily traffic were collected using an automatic measurement system, which triggers above a given threshold of recorded strains. Using the measured strain data due to daily traffic, a bridge signature was created using nonparametric statistical techniques. Maximum experimental strain values from heavy truck events were used to establish a nonparametric probability distribution that describes the behavior of the undamaged bridge under normal operating conditions. Nonparametric prediction intervals were added to the bridge signature, which define where future distributions of strain data from the undamaged bridge should fall. To study the robustness of this method for use in damage detection, three damage scenarios were simulated using a calibrated finite-element model. Comparison of the prediction intervals of the undamaged bridge signature to the analytical damaged distributions showed that, for all three damage scenarios, the damaged distributions fell outside of those intervals, which indicates that this method can potentially identify the presence of structural damage. This study shows that the proposed method is robust and computationally efficient for operational bridge damage detection using only measured strain data from truck loadings.
Statistical Bridge Signatures
Instrumentation of bridge structures provides a stream of data representing operational structural response under loading. The authors define the term bridge signature as the expected response of a particular bridge under loading, as measured by different instruments. In this research, the authors propose a new method to develop and evaluate a bridge signature. The signature can be monitored over time and statistically evaluated to detect potential structural deterioration and damage. An instrumentation system was implemented on the Powder Mill Bridge in Barre, Massachusetts, as a research prototype for the development of a structural health monitoring (SHM) system. Heavy truck events due to daily traffic were collected using an automatic measurement system, which triggers above a given threshold of recorded strains. Using the measured strain data due to daily traffic, a bridge signature was created using nonparametric statistical techniques. Maximum experimental strain values from heavy truck events were used to establish a nonparametric probability distribution that describes the behavior of the undamaged bridge under normal operating conditions. Nonparametric prediction intervals were added to the bridge signature, which define where future distributions of strain data from the undamaged bridge should fall. To study the robustness of this method for use in damage detection, three damage scenarios were simulated using a calibrated finite-element model. Comparison of the prediction intervals of the undamaged bridge signature to the analytical damaged distributions showed that, for all three damage scenarios, the damaged distributions fell outside of those intervals, which indicates that this method can potentially identify the presence of structural damage. This study shows that the proposed method is robust and computationally efficient for operational bridge damage detection using only measured strain data from truck loadings.
Statistical Bridge Signatures
Follen, Christopher W. (author) / Sanayei, Masoud (author) / Brenner, Brian R. (author) / Vogel, Richard M. (author)
2014-01-28
Article (Journal)
Electronic Resource
Unknown
| British Library Online Contents | 2014
| British Library Conference Proceedings | 1995
Classification of Damaged and Modified Bridge Beams with Vibration Signatures
| British Library Online Contents | 2002
Classification of Damaged and Modified Bridge Beams with Vibration Signatures
| British Library Conference Proceedings | 2002
Statistical modeling of bridge approach settlement
| British Library Conference Proceedings