Objective Performance Assessment Using Artificial Neural Networks
Weinstein, Jordan.
2018
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Abstract: Bridge
behavior is used as an objective, data-driven indicator of the performance of bridges. A
framework with which bridge behavior can be identified and learned is presented, and a
method of long-term damage identification using the expected bridge behavior is
introduced. At the Powder Mill Bridge (PMB) in Barre, Massachusetts, strains at each
strain gage location are recorded ... read moreduring operational traffic events. Bridge behavior is
defined as each sensor location's range of expected peak strain during a traffic event
based on all other measured strains at the time at which it experiences its peak strain.
Artificial neural networks (ANNs) are trained with operational data in a bootstrapping
scheme to generate a probabilistic model of bridge behavior. When tested against new
data, the ANN-learned model of bridge behavior is validated for a variety of traffic
events with unknown loading conditions. Structural damage is one way that bridge
behavior, an indicator of performance, of a bridge can change. Damage scenarios are
simulated in a finite element model (FEM) which is calibrated to PMB truck load test
data. The effects of damage are extracted from FEM truck runs and applied to operational
data to assess the capability of the proposed damage identification method through a
series of trials. It is effective at detecting damage, with no Type I and no Type II
errors when using a Wilcoxon rank-sum test of an appropriate significance level. Damage
is effectively localized for two out of three damage
scenarios.
Thesis (M.S.)--Tufts University, 2018.
Submitted to the Dept. of Civil Engineering.
Advisors: Masoud Sanayei, and Brian Brenner.
Committee: Masoud Sanayei, Brian Brenner, and Erin Bell.
Keyword: Civil engineering.read less - ID:
- df65vm12f
- Component ID:
- tufts:24338
- To Cite:
- TARC Citation Guide EndNote
