Using committees of artificial neural networks with finite element modeling for steel girder bridge load rating estimation-Reference-Cited by-同舟云学术

Using committees of artificial neural networks with finite element modeling for steel girder bridge load rating estimation

Published:2021-10 Issue: Volume:33 Page:533-553
ISSN:2352-0124
Container-title:Structures
language:en
Short-container-title:Structures

Author:

Sofi Fayaz A.^ORCID,Steelman Joshua S.^ORCID

Funder

BMS College of Engineering

University of Nebraska-Lincoln

Office of Research and Economic Development, University of Nebraska-Lincoln

Publisher

Elsevier BV

Subject

Safety, Risk, Reliability and Quality,Building and Construction,Architecture,Civil and Structural Engineering

Reference71 articles.

1. American Association of State Highway and Transportation Officials (AASHTO). AASHTO LRFD bridge design specifications. 8th ed. Washington, DC: 2017.

2. American Association of State Highway and Transportation Officials (AASHTO). Manual for bridge evaluation. 3rd ed. 2018.

3. Load distribution and impact factors for I-girder bridges;Kim;J Bridg Eng,1997

4. Quantifying field-test behavior for rating steel girder bridges;Barker;J Bridg Eng,2001

5. Nowak AS, Eom J. Verification of girder distribution factors for steel girder bridges. MDOT 2000-0341 DIR. Michigan DOT, Lansing, MI: 2001.

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