Artificial neural network model to estimate the long-term carbonation depth of concrete exposed to natural environments-Reference-Cited by-同舟云学术

Artificial neural network model to estimate the long-term carbonation depth of concrete exposed to natural environments

Published:2023-09 Issue: Volume:74 Page:106545
ISSN:2352-7102
Container-title:Journal of Building Engineering
language:en
Short-container-title:Journal of Building Engineering

Author:

Majlesi Arsalan^ORCID,Khodadadi Koodiani Hamid^ORCID,Troconis de Rincon Oladis^ORCID,Montoya Arturo^ORCID,Millano Valentina,Torres-Acosta Andres A.^ORCID,Rincon Troconis Brendy C.^ORCID

Publisher

Elsevier BV

Subject

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

Reference92 articles.

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3. Predicting carbonation coefficient using Artificial neural networks and genetic programming;Londhe;J. Build. Eng.,2021

4. Numerical study on the effect of transverse crack self-healing on the corrosion rate of steel bar in concrete;Peng;J. Build. Eng.,2021

5. A numerical study of pitting corrosion in reinforced concrete structures;El Alami;J. Build. Eng.,2021

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