Bridge load rating is a procedure to determine the live load carrying capacity of a bridge. This rating is generally given out on a two-year period, which leaves the structural capacity unknown for this time interval. Conventional bridge load rating is obtained according to the bridge inspection results and commercial bridge rating software. However, this approach cannot effectively reflect actual live load carrying performance of the bridge, due to intrinsic limitation of visual inspection. Structural sensing has been utilized for measuring realistic structural behaviors to reflect the live load carrying capacity. However, this expensive and time-consuming process requires a known-weight vehicle and a substantial number of sensors under controlled full-scale field test conditions. In this research, a continuous live load performance index (LLPI) is proposed to monitor the live load capacity that the bridge can withstand without knowing the vehicle weight while also using a limited number of sensors. The LLPI uses existing bridge load rating methodology, in conjunction with experimental data and numerical simulations, to generate a value that describes the performance of the bridge due directly to the live load applied. Furthermore, the LLPI procedure utilizes an advanced state estimation algorithm, known as the Kalman Filter, to estimate the strain responses of the bridge at various locations while using a limited number of sensors. This procedure allows for an efficient structural health monitoring approach to determine the live load carrying capacity that the bridge can withstand. This research uses a lab-scaled truss structure with known properties for numerical and experimental validation. Because of this, this paper proposes a framework as to which the live load carrying performance can be monitored in real time. Future updates include testing on a real-life bridge structure while also determining optimal sensor placement for obtaining the LLPI. This research looks to develop a new live load performance index (LPPI) by considering: (1) the benefits and limitations of conventional bridge load rating approach, (2) the system identification and multi-metric data acquisition for the bridge structure, (3) numerical modeling and updating to best reflect the current dynamic properties of the bridge, (4) augmented Kalman Filter to estimate structural responses at various unknown locations, (5) LLPI formulation using experimental data, current bridge load rating methodology, and model-response estimations. The results obtained from this research provide a progressive live load capacity performance template to promote the advancement in civil infrastructure smart monitoring.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/626386 |
| Date | January 2017 |
| Creators | Walcker, Andrew Jon, Walcker, Andrew Jon |
| Contributors | Jo, Hongki, Jo, Hongki, Haldar, Achintya, Fleischman, Robert |
| Publisher | The University of Arizona. |
| Source Sets | University of Arizona |
| Language | en_US |
| Detected Language | English |
| Type | text, Electronic Thesis |
| Rights | Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. |
Page generated in 0.0028 seconds