Performance assessment of prestressed concrete bridge girders using fiber optic sensors and artificial neural networks-Reference-Cited by-同舟云学术

Performance assessment of prestressed concrete bridge girders using fiber optic sensors and artificial neural networks

Published:2020-05-19 Issue:5 Volume:17 Page:605-619
ISSN:1573-2479
Container-title:Structure and Infrastructure Engineering
language:en
Short-container-title:Structure and Infrastructure Engineering

Author:

Khandel Omid¹^ORCID,Soliman Mohamed¹^ORCID,Floyd Royce W.²,Murray Cameron D.³^ORCID

Affiliation:

1. School of Civil and Environmental Engineering, Oklahoma State University, Stillwater, OK, USA

2. School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK, USA

3. Department of Civil Engineering, University of Arkansas, Fayetteville, AR, USA

Funder

Southern Plains Transportation Center

Publisher

Informa UK Limited

Subject

Mechanical Engineering,Ocean Engineering,Geotechnical Engineering and Engineering Geology,Safety, Risk, Reliability and Quality,Building and Construction,Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/15732479.2020.1759658

Reference34 articles.

1. Analysis of the status of pre-release cracks in prestressed concrete structures using long-gauge sensors

2. Monitoring of long-term prestress losses in prestressed concrete structures using fiber optic sensors

3. Damage identification using modal strains identified from operational fiber-optic Bragg grating data

4. Evaluating the early-age behaviour of full-scale prestressed concrete beams using distributed and discrete fibre optic sensors

5. Full-scale test of a concrete box girder using FBG sensing system

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