Research on the recurrent neural network-based fatigue damage model of asphalt binder and the finite element analysis development-Reference-Cited by-同舟云学术

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Research on the recurrent neural network-based fatigue damage model of asphalt binder and the finite element analysis development

Published:2021-01 Issue: Volume:267 Page:121761
ISSN:0950-0618
Container-title:Construction and Building Materials
language:en
Short-container-title:Construction and Building Materials

Author:

Yang Enhui,Tang Youzhi,Li Li,Yan Wei,Huang Bing,Qiu Yanjun

Funder

Sichuan Yakang Expressway Co., Ltd.

Publisher

Elsevier BV

Subject

General Materials Science,Building and Construction,Civil and Structural Engineering

Reference38 articles.

1. Analysis of linear viscoelastic response function model for asphalt binders;Shan;J. Mater. Civ. Eng.,2016

2. Introducing a stress-dependent fractional nonlinear viscoelastic model for modified asphalt binders;Hajikarimi;Constr. Build. Mater.,2018

3. Improved interpretation of asphalt binder parallel plate dynamic shear rheometer fatigue tests;Safaei;Int. J. Pavement Eng.,2020

4. A theory of mechanical behavior of elastic media with growing damage and other changes in structure;Schapery;J. Mech. Phys. Solids,1990

5. Viscoelastic continuum damage (vecd) models for cracking problems in asphalt mixtures;Kutay;Int. J. Pavement Eng.,2018

Cited by 13 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Fractional viscoelastic constitutive modelling of real-time strain response for asphalt pavement composites subjected to simulating wheel loadings;International Journal of Pavement Engineering;2024-07-30

2. Application of Machine Learning and Deep Learning in Finite Element Analysis: A Comprehensive Review;Archives of Computational Methods in Engineering;2024-03-01

3. PINN-AFP: A novel C-S curve estimation method for asphalt mixtures fatigue prediction based on physics-informed neural network;Construction and Building Materials;2024-02

4. Development of Plasticity-Controlled Failure Prediction Model Of Asphalt Binder;2024

5. Real-time indoor thermal comfort prediction in campus buildings driven by deep learning algorithms;Journal of Building Engineering;2023-11

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