Influence of interface transition zone on effective elastic property of heterogeneous materials with an artificial neural network study

被引:1
|
作者
Xue, Jing [1 ]
Cao, Yajun [2 ]
Burlion, Nicolas [1 ]
Shao, Jianfu [1 ,3 ]
机构
[1] Univ Lille, CNRS, Cent lille, LaMcube, Lille, France
[2] Hohai Univ, Key Lab Minist Educ Geomech & Embankment Engn, Nanjing, Peoples R China
[3] Univ Lille, CNRS, Cent Lille, LaMcube, F-59000 Lille, France
关键词
artificial neural network; concrete; cement-based material; effective elastic properties; interface transition zone; multi-scale modeling; NUMERICAL-METHOD; CEMENT PASTE; BULK MODULUS; CONCRETE; AGGREGATE; PREDICTION; STRENGTH; MODEL; MICROSTRUCTURE; DIFFUSIVITY;
D O I
10.1002/nag.3508
中图分类号
P5 [地质学];
学科分类号
0709 ; 081803 ;
摘要
In this study, the influence of interface transition zone (ITZ) on the elastic properties of concrete and rock like heterogeneous materials is investigated. Direct simulations of a representative volume element are realized by using a fast Fourier transform based method and the obtained results are used as the reference solutions. Some widely used analytical homogenization models are evaluated by comparing the theoretical predictions with the reference solutions. Based on this evaluation, an artificial neural network (ANN) model is constructed in order to improve the analytical models. The proposed ANN model is trained, tested and validated against the reference solution. Its efficiency and good accuracy are clearly demonstrated.
引用
收藏
页码:1134 / 1151
页数:18
相关论文
共 38 条
  • [31] Strength evaluation of CF/PEEK resistance welding based on improved artificial neural network: Interface failure mechanism study under extreme service temperatures
    Shen, Liangliang
    Zhang, Yi
    Zhang, Heyuan
    Sun, Mingxin
    Jian, Xigao
    Xu, Jian
    COMPOSITES PART B-ENGINEERING, 2025, 297
  • [32] Investigations on the relationship among the porosity, permeability and pore throat size of transition zone samples in carbonate reservoirs using multiple regression analysis, artificial neural network and adaptive neuro-fuzzy interface system (vol 6, pg 321, 2021)
    Adegbite, Jamiu Oyekan
    Belhaj, Hadi
    Bera, Achinta
    PETROLEUM RESEARCH, 2023, 8 (02) : 283 - 283
  • [33] Quantitative structure-property relationship (QSPR) study to predict retention time of polycyclic aromatic hydrocarbons using the random forest and artificial neural network methods
    Moona Emrarian
    Mahmoud Reza Sohrabi
    Nasser Goudarzi
    Fariba Tadayon
    Structural Chemistry, 2020, 31 : 1281 - 1288
  • [34] Quantitative structure-property relationship (QSPR) study to predict retention time of polycyclic aromatic hydrocarbons using the random forest and artificial neural network methods
    Emrarian, Moona
    Sohrabi, Mahmoud Reza
    Goudarzi, Nasser
    Tadayon, Fariba
    STRUCTURAL CHEMISTRY, 2020, 31 (04) : 1281 - 1288
  • [35] Model prediction of depth-specific soil texture distributions with artificial neural network: A case study in Yunfu, a typical area of Udults Zone, South China
    Ding, Xiaogang
    Zhao, Zhengyong
    Yang, Qi
    Chen, Lina
    Tian, Qiuyan
    Li, Xiaochuan
    Meng, Fan-Rui
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2020, 169
  • [36] Boosting-Crystal Graph Convolutional Neural Network for Predicting Highly Imbalanced Data: A Case Study for Metal-Insulator Transition Materials
    Kim, Eun Ho
    Gu, Jun Hyeong
    Lee, June Ho
    Kim, Seong Hun
    Kim, Jaeseon
    Shin, Hyo Gyeong
    Kim, Shin Hyun
    Lee, Donghwa
    ACS APPLIED MATERIALS & INTERFACES, 2024, 16 (33) : 43734 - 43741
  • [37] Enhanced photo-degradation of N-methyl-2-pyrrolidone (NMP): Influence of matrix components, kinetic study and artificial neural network modelling
    Kumar P.
    Verma S.
    Kaur R.
    Papac J.
    Kušić H.
    Štangar U.L.
    Journal of Hazardous Materials, 2022, 434
  • [38] Exploring the Influence of Dynamic Indicators in Urban Spaces on Residents' Environmental Behavior: A Case Study in Shanghai Utilizing Mixed-Methods Approach and Artificial Neural Network (ANN) Modeling
    Lyu, Chengzhe
    SUSTAINABILITY, 2024, 16 (08)