Cloud-based Real-time Continuous Bridge Monitoring: Bridge Weigh in Motion and Condition Assessment
Zhao, Zhiyong.
2017
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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... read moreDistribution 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
