Potential Improvement in Rating Factors of Concrete T-Beam Bridges through Refined Analysis: Evaluation of Distribution Factors-Reference-Cited by-同舟云学术

Potential Improvement in Rating Factors of Concrete T-Beam Bridges through Refined Analysis: Evaluation of Distribution Factors

Published:2022-09 Issue:9 Volume:27 Page:
ISSN:1084-0702
Container-title:Journal of Bridge Engineering
language:en
Short-container-title:J. Bridge Eng.

Author:

Ndong Abdou K.¹,Sherif Muhammad M.²,Kassner Bernie³,Harris Devin K.⁴^ORCID,Ozbulut Osman E.⁵^ORCID

Affiliation:

1. Graduate Student, Dept. of Engineering Systems and Environment, 351 McCormick Rd., Univ. of Virginia, Charlottesville, VA 22904.

2. Assistant Professor, Dept. of Civil, Construction, and Environmental Engineering, Univ. of Alabama at Birmingham, Birmingham, AL 35294.

3. Research Scientist, Virginia Transportation Research Council, Virginia Dept. of Transportation, 530 Edgemont Rd., Charlottesville, VA 22903.

4. Professor, Dept. of Engineering Systems and Environment, 351 McCormick Rd., Univ. of Virginia, Charlottesville, VA 22904. ORCID: .

5. Associate Professor, Dept. of Engineering Systems and Environment, 351 McCormick Rd., Univ. of Virginia, Charlottesville, VA 22904 (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Building and Construction,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29BE.1943-5592.0001928

Reference33 articles.

1. AASHTO. 2002. Standard specifications for highway bridges. 17th ed. Washington, DC: AASHTO.

2. AASHTO. 2010. AASHTO LRFD bridge design specifications. 5th ed. Washington, DC: AASHTO.

3. AASHTO. 2015. Manual for bridge evaluation. 2nd ed. with 2016 Interim Revisions. Washington, DC: AASHTO.

4. Adams, A., N. Galindez, T. Hopper, T. Murphy, P. Ritchie, V. Storlie, and J. Weisman. 2019. Manual for refined analysis in bridge design and evaluation. FHWA-HIF-18-046. Washington, DC: United States. Federal Highway Administration. Office of Infrastructure.

5. K-SVCR. A support vector machine for multi-class classification;Angulo C.;Neurocomputing,2003

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