Frequencies Prediction of Laminated Timber Plates Using ANN Approach

作     者：Jianping Sun Jan Niederwestberg Fangchao Cheng Yinghei Chui 

作者机构：School of ResourcesEnvironment and MaterialsGuangxi UniversityNanning530004China Department of Civil and Environmental EngineeringUniversity of AlbertaEdmontonABT6G 1H9Canada 

出 版 物：《Journal of Renewable Materials》 (可再生材料杂志（英文）)

年 卷 期：2020年第8卷第3期

页      面：319-328页

核心收录：

学科分类：08[工学] 080102[工学-固体力学] 0801[工学-力学（可授工学、理学学位）] 

基　　金：supported by National Natural Science Foundation of China(Project No.31660174) Guangxi Innovation-Driven Development Special Fund Project of China(Project No.AA17204087-16) through funding to NSERC Strategic Network on Innovative Wood Products and Building System,by the Natural Sciences and Engineering Research Council of Canada 

主　　题：Cross laminated timber(CLT) vibration test natural frequency wavelet analysis artificial neural network(ANN) 

摘      要：Cross laminated timber(CLT)panels,which are used as load bearing plates and shear panels in timber structures,can serve as roofs,walls and *** timber is construction material with relatively less stiffness,the design of such structures is often driven by serviceability criteria,such as deflection and ***,accurate vibration and elastic properties are vital for engineered CLT *** objective of this research is to explore a method to determine the natural frequencies of orthotropic wood plates efficiently and *** method was developed based on vibration signal processing by wavelet to acquire the effective sample data,and a model developed by artificial neural network(ANN)to achieve the prediction of nature ***,experiments were performed to obtain vibration signals of single-layer *** vibration signals were then processed by wavelet packet transform to extract the eigenvectors,which served as the samples to train the ANN *** trained model was employed to predict three nature frequencies of other test *** results showed that the proposed method can produce predicted frequencies fast and efficiently within 10%of the measured values.