In-service monitoring of US202/NJ23 highway overpass: Four-year overview

D. H. Sigurdardottir, K. Flanigan, B. Glisic

Research output: Contribution to conferencePaperpeer-review

Abstract

The US202/NJ23 highway overpass is located close to Wayne, New Jersey, USA. The structure is composed of two separate northbound and southbound bridges. Steel girders support a concrete deck in several simple spans. The girders are built-up sections with varying flange thickness. Structurally and functionally, the US202/NJ23 overpass is a good example of a typical bridge frequently found across the USA. Therefore it provides an excellent opportunity to test performance of Structural Health Monitoring (SHM) applied on typical structures. Varying cross-sections and varying girder lengths, along with several perpendicular diaphragms create a complex structure, and SHM facilitates assessment of its structural behavior. The bridge is included in the FHWA's Long-Term Bridge Performance Program. An SHM system based on long-gauge Fiber Bragg-grating sensors was installed in June 2011. Since then 23 visits have been made to the bridge and short-term dynamic data were collected. In this paper a four-year overview of this monitoring project is presented including installation of the SHM system, evaluation of neutral axis as damage sensitive feature, evaluation of natural frequencies and damping from strain measurements, construction of curvature and moment diagrams, and temperature analysis. Unusual behavior was detected at one location through analysis of the neutral axis location.

Original languageEnglish (US)
StatePublished - 2015
Event7th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2015 - Torino, Italy
Duration: Jul 1 2015Jul 3 2015

Other

Other7th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2015
Country/TerritoryItaly
CityTorino
Period7/1/157/3/15

All Science Journal Classification (ASJC) codes

  • Building and Construction
  • Civil and Structural Engineering
  • Artificial Intelligence

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