Ensemble wavelet-learning approach for predicting the effective mechanical properties of concrete composite materials

doi:10.1007/s00466-022-02170-1

CSpace

	Ensemble wavelet-learning approach for predicting the effective mechanical properties of concrete composite materials
	Linghu, Jiale 1; Dong, Hao 1; Cui, Junzhi 2
	2022-04-18
发表期刊	COMPUTATIONAL MECHANICS
ISSN	0178-7675
页码	31
摘要	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.
关键词	Concrete composite materials Weibull distribution Equivalent mechanical parameters Artificial neural networks Wavelet transform
DOI	10.1007/s00466-022-02170-1
收录类别	SCI
语种	英语
资助项目	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研究方向	Mathematics ; Mechanics
WOS类目	Mathematics, Interdisciplinary Applications ; Mechanics
WOS记录号	WOS:000784384400001
出版者	SPRINGER
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/60302
专题	中国科学院数学与系统科学研究院
通讯作者	Dong, Hao
作者单位	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
推荐引用方式 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.