Evaluating the effect of curing conditions on the glass transition of the structural adhesive using conditional tabular generative adversarial networks-Reference-Cited by-同舟云学术

Evaluating the effect of curing conditions on the glass transition of the structural adhesive using conditional tabular generative adversarial networks

Published:2024-04 Issue: Volume:130 Page:107796
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Wang Songbo^ORCID,Yang Haixin^ORCID,Stratford Tim^ORCID,He Jiayi,Li Biao,Su Jun

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference50 articles.

1. Viscoelastic model to capture residual stresses in heat cured dissimilar adhesive bonded joints;Agha;Int. J. Adhesion Adhes.,2021

2. Machine learning prediction of structural response of steel fiber-reinforced concrete beams subjected to far-field blast loading;Almustafa;Cem. Concr. Compos.,2022

3. Hygrothermal durability of epoxy adhesives used in civil structural applications;Ascione;Compos. Struct.,2021

4. The influence of the hygrothermal aging on the strength and stiffness of adhesives used for civil engineering applications with pultruded profiles: an experimental and numerical investigation;Ascione;J. Adhes.,2022

5. Genetic programming based symbolic regression for shear capacity prediction of SFRC beams;Ben Chaabene;Construct. Build. Mater.,2021

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