Machine learning approach for analysing and predicting the modulus response of the structural epoxy adhesive at elevated temperatures-Reference-Cited by-同舟云学术

Machine learning approach for analysing and predicting the modulus response of the structural epoxy adhesive at elevated temperatures

Published:2023-02-24 Issue:16 Volume:99 Page:2280-2298
ISSN:0021-8464
Container-title:The Journal of Adhesion
language:en
Short-container-title:The Journal of Adhesion

Author:

Wang Songbo¹²,Xu Ziyang¹²,Stratford Tim³,Li Biao¹²,Zeng Qingdian¹²,Su Jun¹²

Affiliation:

1. School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan, China

2. Innovation Demonstration Base of Ecological Environment Geotechnical and Ecological Restoration of Rivers and Lakes, Hubei University of Technology, Wuhan, China

3. School of Engineering, Institute for Infrastructure and Environment, the University of Edinburgh, Edinburgh, UK

Funder

Hubei University of Technology

Publisher

Informa UK Limited

Subject

Materials Chemistry,Surfaces, Coatings and Films,Surfaces and Interfaces,Mechanics of Materials,General Chemistry

Link

https://www.tandfonline.com/doi/pdf/10.1080/00218464.2023.2183851

Reference44 articles.

1. Strengthening of steel structures with fiber-reinforced polymer composites

2. A state-of-the-art review: Near-surface mounted FRP composites for reinforced concrete structures

3. A comparison of the influence of nonlinear and linear creep on the behaviour of FRP-bonded metallic beams at warm temperatures

4. Mechanical Characterization of Flexible Adhesives

5. A parametric study of adhesive bonded joints with composite material using black-box and grey-box machine learning methods: Deep neuron networks and genetic programming

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