Cloud-based Real-time Continuous Bridge Monitoring: Bridge Weigh in Motion and Condition Assessment
Zhao, Zhiyong.
2017-04-18T11:39:52.787Z
-
Abstract: A cloud-based real-time bridge monitoring system was proposed in the research. Two possible applications of the real-time bridge monitoring system were studied. The first application used operational strain measurements to identify truck travel path, velocity, axle configuration, and total truck weight. The truck path is estimated by interpolating the relationship between the Girder Dist... read moreribution Factors (GDFs) and truck path location. The truck velocity and axle configuration are estimated from the second derivative of strain measurements. The total truck weight is estimated using an influence line calibrated from a diagnostic load test performed with a known truck. The method is verified using strain data measured from daily truck traffic with known weights. The second application utilized detrended operational measured GDFs to detect possible bridge damages. A multiple regression model is fitted to the GDFs to study the factors that affect the GDFs. The multiple regression revealed that bridge age, temperature, frozen ground, and vehicle travel path are statistically significant explanatory variables for explaining most of the observed variability in GDFs. Using that regression model, the variations due to environmental factors and traffic events are removed from GDFs to eliminate those factors, leaving only the live load distribution due to the geometry of the bridge. A nonparametric rank-sum test is used to detect damage based on the resulting detrended GDFs. The bootstrap method is used to develop bridge signatures which assess the location of the damage. The proposed method is shown to exhibit a high degree of statistical power for detecting damages using operational detrended GDFs from strain measurements, resulting in both low probabilities of Type I and Type II errors.
Thesis (M.S.)--Tufts University, 2017.
Submitted to the Dept. of Civil Engineering.
Advisor: Masoud Sanayei.
Committee: Richard Vogel, and Alva Couch.
Keyword: Civil engineering.read less - ID:
- ft849257k
- Component ID:
- tufts:20684
- To Cite:
- DCA Citation Guide EndNote
