On developing accurate prediction models for residual tensile strength of GFRP bars under alkaline-concrete environment using a combined ensemble machine learning methods-Reference-Cited by-同舟云学术

On developing accurate prediction models for residual tensile strength of GFRP bars under alkaline-concrete environment using a combined ensemble machine learning methods

Published:2023-07 Issue: Volume:18 Page:e02157
ISSN:2214-5095
Container-title:Case Studies in Construction Materials
language:en
Short-container-title:Case Studies in Construction Materials

Author:

Go Chaeyeon,Kwak Yun-Ji,Kwag Shinyoung^ORCID,Eem Seunghyun,Lee Sangwoo,Ju Bu-Seog

Funder

Ministry of Science, ICT and Future Planning

National Research Foundation of Korea

Publisher

Elsevier BV

Subject

Materials Science (miscellaneous)

Reference56 articles.

1. Durability of fiber reinforced polymer (FRP) in simulated seawater sea sand concrete (SWSSC) environment;Guo;Corros. Sci.,2018

2. Bond behavior between BFRP rebar and seawater sea sand concrete;Wu;Adv. Civ. Eng.,2020

3. Challenges and opportunities in corrosion of steel in concrete;Angst;Mater. Struct.,2018

4. Use of fiber reinforced polymer composites as reinforcing material for concrete;Uomoto;J. Mater. Civ. Eng.,2002

5. Investigation of seawater effects on the mechanical properties of untreated and treated MMT-based glass fiber/vinylester composites;Mittal;Ocean Eng.,2015

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