KMS Of Academy of mathematics and systems sciences, CAS
| Ensemble wavelet-learning approach for predicting the effective mechanical properties of concrete composite materials | |
| Linghu, Jiale1; Dong, Hao1; Cui, Junzhi2 | |
| 2022-04-18 | |
| Source Publication | COMPUTATIONAL MECHANICS
 |
| ISSN | 0178-7675 |
| Pages | 31 |
| Abstract | This paper proposes a high-accuracy and efficient ensemble wavelet-neural network method to predict the equivalent mechanical parameters of concrete composites. The doubly random uncertainties in structural heterogeneities and mechanical properties of concrete composites result in a challenging task to handle high-dimensional data properties, highly-complex mappings and huge computational cost for the repeated prediction of their mechanical parameters. The significant characteristics of this study are: (i) The random uncertainties both of structural heterogeneities and mechanical properties of concrete composites are modeled based on authors' previous work and Weibull probabilistic model, respectively. (ii) Asymptotic homogenization method (AHM) and the proposed background mesh technique are introduced to thoroughly extract the doubly random geometric and material characteristics of concrete composites for establishing concrete material databases. (iii) The wavelet transform is used to preprocess the high-dimensional data features of the material database, and the wavelet coefficients are used as the new input neurons of the artificial neural network (ANN) to establish the ensemble wavelet-neural network model. It should be noted that the wavelet-based learning strategy can not only extract important data features and resist noise from material database, but also achieve a great reduction in input data of neural networks from the entire material database and ensuring the successful training the neural networks. Finally, numerical experiments indicate that the proposed ensemble approach is a robust method for the high-accuracy and efficient prediction of equivalent mechanical properties of concrete composites. |
| Keyword | Concrete composite materials Weibull distribution Equivalent mechanical parameters Artificial neural networks Wavelet transform |
| DOI | 10.1007/s00466-022-02170-1 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[51739007] ; National Natural Science Foundation of China[61971328] ; National Natural Science Foundation of China[12001414] ; Fundamental Research Funds for the Central Universities[JB210702] ; open foundation of Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics (Wuhan University of Technology)[WUT-TAM202104] ; Key Technology Research of FRP-Concrete Composite Structure ; Center for high performance computing of Xidian University |
| WOS Research Area | Mathematics ; Mechanics |
| WOS Subject | Mathematics, Interdisciplinary Applications ; Mechanics |
| WOS ID | WOS:000784384400001 |
| Publisher | SPRINGER |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/60302 |
| Collection | 中国科学院数学与系统科学研究院 |
| Corresponding Author | Dong, Hao |
| Affiliation | 1.Xidian Univ, Sch Math & Stat, 2 South Taibai Rd, Xian 710071, Shaanxi, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, 55 East Zhongguancun Rd, Beijing 100190, Peoples R China |
| Recommended Citation GB/T 7714 | Linghu, Jiale,Dong, Hao,Cui, Junzhi. Ensemble wavelet-learning approach for predicting the effective mechanical properties of concrete composite materials[J]. COMPUTATIONAL MECHANICS,2022:31. |
| APA | Linghu, Jiale,Dong, Hao,&Cui, Junzhi.(2022).Ensemble wavelet-learning approach for predicting the effective mechanical properties of concrete composite materials.COMPUTATIONAL MECHANICS,31. |
| MLA | Linghu, Jiale,et al."Ensemble wavelet-learning approach for predicting the effective mechanical properties of concrete composite materials".COMPUTATIONAL MECHANICS (2022):31. |
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