Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Probabilistic structural integrity evaluation of a highway steel bridge under unknown trucks

Probabilistic structural integrity evaluation of a highway steel bridge under unknown trucks AbstractThis paper presents fragility curves derived from a best-fit regression model that enable to quantify probabilistic structural integrity of an in-service highway steel bridge under multiple truck passages with uncertain characteristics. The regression model is able to be identified via analytical modelling techniques incorporating the bridge response quantities resulting from unknown five-axle trucks through structural health monitoring system. These quantities coupled with weigh-in-motion (WIM) data obtained from two weigh stations closest to the bridge are used (1) to identify which unknown trucks are presumably to travel over the bridge and (2) to quantify the plausible characteristics of the trucks and the corresponding load ratings determined following the AASHTO Manual. With this information, the regression models can be made to demonstrate load ratings as the truck characteristics change. Based on the best-fitted load ratings, nine significant truck characteristics were looked at in their role in causing a below satisfactory bridge capacity. Key findings reveal that the all axle weights affect the fragility curves though the axle weights are not significantly different, and the most significant axle spacing is the first spacing. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Structural Integrity and Maintenance Taylor & Francis

Probabilistic structural integrity evaluation of a highway steel bridge under unknown trucks

Download PDF
8 pages

Probabilistic structural integrity evaluation of a highway steel bridge under unknown trucks

Abstract

AbstractThis paper presents fragility curves derived from a best-fit regression model that enable to quantify probabilistic structural integrity of an in-service highway steel bridge under multiple truck passages with uncertain characteristics. The regression model is able to be identified via analytical modelling techniques incorporating the bridge response quantities resulting from unknown five-axle trucks through structural health monitoring system. These quantities coupled with...
Loading next page...
 
/lp/taylor-francis/probabilistic-structural-integrity-evaluation-of-a-highway-steel-nxvrtnsFk0
Publisher
Taylor & Francis
Copyright
2016 Korea Institute for Structural Maintenance and Inspection
ISSN
2470-5322
eISSN
2470-5314
DOI
10.1080/24705314.2016.1179495
Publisher site
See Article on Publisher Site

Abstract

AbstractThis paper presents fragility curves derived from a best-fit regression model that enable to quantify probabilistic structural integrity of an in-service highway steel bridge under multiple truck passages with uncertain characteristics. The regression model is able to be identified via analytical modelling techniques incorporating the bridge response quantities resulting from unknown five-axle trucks through structural health monitoring system. These quantities coupled with weigh-in-motion (WIM) data obtained from two weigh stations closest to the bridge are used (1) to identify which unknown trucks are presumably to travel over the bridge and (2) to quantify the plausible characteristics of the trucks and the corresponding load ratings determined following the AASHTO Manual. With this information, the regression models can be made to demonstrate load ratings as the truck characteristics change. Based on the best-fitted load ratings, nine significant truck characteristics were looked at in their role in causing a below satisfactory bridge capacity. Key findings reveal that the all axle weights affect the fragility curves though the axle weights are not significantly different, and the most significant axle spacing is the first spacing.

Journal

Journal of Structural Integrity and Maintenance – Taylor & Francis

Published: Apr 2, 2016

Keywords: Bridge; regression model; load rating; weigh-in-motion data; fragility curve; sensitivity analysis

References

  • WIM-based live load for bridges
  • Traffic load modeling and factors influencing the accuracy of predicte d extremes
  • Bridge rating protocol using ambient trucks through structural health monitoring system
  • Integrated structural health monitoring system and multi-regression models for determining load ratings for complex steel bridges
  • Use of response surface metamodels to generate system level fragilities for existing curved steel bridges
  • Horizontally curved steel bridge seismic vulnerability assessment
  • Seismic fragility methodology for highway bridges using a component level approach
  • A bridge structural health monitoring system using statistical control chart analysis