TY - GEN
T1 - Use of structural health monitoring system for assessment of bridge load rating
AU - Seo, Junwon
AU - Phares, Brent M.
AU - Lu, Ping
AU - Wipf, Terry J.
AU - Dahlberg, Justin
PY - 2013
Y1 - 2013
N2 - A technical framework that uses a Structural Health Monitoring (SHM) system, which continuously measures bridge response to unknown ambient trucks, was proposed to calculate load ratings based upon finite element model simulations coupled with a statistical backbone. A steel bridge located in Iowa was selected to demonstrate this technical framework. Critical locations of the bridge were instrumented with a network of fiber optic sensors that collect real-time strain data from ambient trucks. As their characteristics were unknown, they were statistically characterized in terms of configuration and weight using Weigh-In-Motion (WIM) data collected in Iowa. Subsets of strain data were randomly selected to optimize computational models created with finite element software. The optimized models were used to determine distributions of load ratings following the AASHTO Load Factor Rating (LFR) methodology. Distributions were created for each strain set. The distributions, which account for variability in unknown trucks, can be used to evaluate the structural capacity of the bridge.
AB - A technical framework that uses a Structural Health Monitoring (SHM) system, which continuously measures bridge response to unknown ambient trucks, was proposed to calculate load ratings based upon finite element model simulations coupled with a statistical backbone. A steel bridge located in Iowa was selected to demonstrate this technical framework. Critical locations of the bridge were instrumented with a network of fiber optic sensors that collect real-time strain data from ambient trucks. As their characteristics were unknown, they were statistically characterized in terms of configuration and weight using Weigh-In-Motion (WIM) data collected in Iowa. Subsets of strain data were randomly selected to optimize computational models created with finite element software. The optimized models were used to determine distributions of load ratings following the AASHTO Load Factor Rating (LFR) methodology. Distributions were created for each strain set. The distributions, which account for variability in unknown trucks, can be used to evaluate the structural capacity of the bridge.
KW - Bridge
KW - Load rating distribution
KW - Optimization
KW - Strain data
KW - Structural health monitoring system
KW - Unknown trucks
UR - http://www.scopus.com/inward/record.url?scp=84873812299&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84873812299&partnerID=8YFLogxK
U2 - 10.1061/9780784412640.003
DO - 10.1061/9780784412640.003
M3 - Conference contribution
AN - SCOPUS:84873812299
SN - 9780784412640
T3 - Forensic Engineering 2012: Gateway to a Better Tomorrow - Proceedings of the 6th Congress on Forensic Engineering
SP - 18
EP - 27
BT - Forensic Engineering 2012
T2 - 6th Congress on Forensic Engineering 2012: Gateway to a Better Tomorrow
Y2 - 31 October 2012 through 3 November 2012
ER -