Design-Oriented Machine-Learning Models for Predicting the Shear Strength of Prestressed Concrete Beams-Reference-Cited by-同舟云学术

Design-Oriented Machine-Learning Models for Predicting the Shear Strength of Prestressed Concrete Beams

Published:2023-04 Issue:4 Volume:28 Page:
ISSN:1084-0702
Container-title:Journal of Bridge Engineering
language:en
Short-container-title:J. Bridge Eng.

Author:

Bedriñana Luis Alberto¹^ORCID,Sucasaca Julio²,Tovar Jhon³,Burton Henry⁴

Affiliation:

1. Assistant Professor, Dept. of Civil Engineering, Univ. de Ingenieria y Tecnologia—UTEC, Jr. Medrano Silva 165, Barranco, Lima 15063, Peru (corresponding author). ORCID: .

2. Research Assistant, Dept. of Civil Engineering, Univ. de Ingenieria y Tecnologia—UTEC, Jr. Medrano Silva 165, Barranco, Lima 15063, Peru.

3. Student Researcher, Dept. of Civil Engineering, Univ. de Ingenieria y Tecnologia—UTEC, Jr. Medrano Silva 165, Barranco, Lima 15063, Peru.

4. Associate Professor, Dept. of Civil and Environmental Engineering, Univ. of California, Los Angeles, CA 90095.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Building and Construction,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JBENF2.BEENG-6013

Reference53 articles.

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

2. ACI (American Concrete Institute). 2019. Building code requirements for structural concrete and commentary. ACI 318-19. Farmington Hills, MI: ACI.

3. Explainable machine learning model for predicting drift capacity of reinforced concrete walls;Aladsani M. A.;ACI Struct. J.,2022

4. Shear capacity of RC members without shear reinforcement: A modified crack sliding model;Autrup F.;Eng. Struct.,2021

5. Explaining the riddle of tension stiffening models for shear panel experiments;Bentz E. C.;J. Struct. Eng.,2005

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