基于人工神经网络模型的木材干燥应变模拟预测

doi:10.11707/j.1001-7488.20200608

Abstract

Abstract:

Objective: The effects of drying schedule, pretreatment condition, moisture content on wood drying stress and its distribution from pith to bark were investigated in this study to achieve the simulation and prediction of drying strain. Methods: The data sets of elastic strain and mechano-sorptive creep obtained with image analysis method were analyzed. The elastic strain and mechano-sorptive creep were modelled by artificial neural network, for the model of elastic strain with four inputs, i.e., drying temperatures, wood moisture content, relative humidity and the distance from pith, and for mechano-sorptive creep added pre-steaming temperature as an additional input. According to the training and validation processes, two reasonable prediction models were obtained, and then the predictive ability of the models were discussed with testing processes. Results: In elastic strain model, there were significant correlations between experimental and predicted values in all date sets. The R-values for training, validation and test sets were 0.988, 0.983 and 0.978, respectively. The R² values were greater than 0.95 in all data sets, and the best validation performance of the mean square error was 1.21×10^-6. In mechano-sorptive creep model, the R-values for training and validation sets were 0.981 and 0.977 with the data sets at moisture content of 28% and 12%, respectively. The best validation performance of the mean square error was 1.26×10^-6. The R-value for test set was 0.969 with the data set at moisture content of 20%. Furthermore, the R² values were greater than 0.94 in all data sets, indicating that the network model was capable to explain more than 94% experimental values. Conclusion: In the two established models, the predicted values were in good agreement with the experimental values, showing a high prediction accuracy, which provided a feasible basis for the application of artificial neural network in exploring drying stress and strain.

Key words: artificial neural network, elastic strain, mechano-sorptive creep, simulation and prediction

CLC Number:

S781.71

Zongying Fu,Yingchun Cai,Xin Gao,Fan Zhou,Jinghui Jiang,Yongdong Zhou. Simulation of Drying Strain Based on Artificial Neural Network Model[J]. Scientia Silvae Sinicae, 2020, 56(6): 76-82.

Figures/Tables 11

Fig.1

Fig.2

Table 1

Fig.3

Fig.4

Table 2

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

References 0

	付宗营, 赵景尧, 蔡英春. 树盘干缩异向性引起应变的测算及分析. 中国工程科学, 2014. 164, 25- 29. doi: 10.3969/j.issn.1009-1742.2014.04.005
	Fu Z Y , Zhao J Y , Cai Y C . Investigation of tangential strain caused by shrinkage anisotropy using image analytical method. Engineering Sciences, 2014. 164, 25- 29. doi: 10.3969/j.issn.1009-1742.2014.04.005
	李坚. 木材科学. 北京: 科学出版社. 2014. 254
	Li J . Wood science. Beijing: Science Press. 2014. 254
	蒋佳荔, 吕建雄. 木材干燥应力的研究方法与进展. 木材工业, 2005. 192, 4- 7.
	Jiang J L , Lü J X . Review of research methods for wood drying stresses. China Wood Industry, 2005. 192, 4- 7.
	杨文斌, 陈眉雯. 利用神经网络预测木材径向导热系数. 林业科学, 2006. 423, 25- 28.
	Yang W B , Chen M W . Predicting the wood radial thermal conductivity using neural network. Scientia Silvae Sinicae, 2006. 423, 25- 28.
	Avramidis S , Iliadis L . Predicting wood thermal conductivity using artificial neural networks. Wood and Fiber Science, 2005a. 37, 682- 690.
	Avramidis S , Iliadis L . Wood-water sorption isotherm prediction with artificial neural networks:a preliminary study. Holzforschung, 2005b. 59, 336- 341. doi: 10.1515/HF.2005.055
	Avramidis S , Iliadis L , Mansfield S D . Wood dielectric loss factor prediction with artificial neural networks. Wood Science and Technology, 2006. 40, 563- 574. doi: 10.1007/s00226-006-0096-3
	Avramidis S , Wu H W . Artificial neural network and mathematical modeling comparative analysis of nonisothermal diffusion of moisture in wood. HolzalsRoh-und Werkstoff, 2007. 65, 89- 93. doi: 10.1007/s00107-006-0113-0
	Bedelean B , Lazarescu C , Avramidis S . Predicting RF heating rate during pasteurization of green softwoods using artificial neural networks and Monte Carlo method. Wood Research, 2015. 601, 83- 94.
	Ceylan I . Determination of drying characteristics of timber by using artificial neural networks and mathematical models. Drying Technology, 2008. 26, 1469- 1476. doi: 10.1080/07373930802412132
	Esteban L G , Fernandez F G , Palacios P D . MOE prediction in Abies pinsapo Boiss. timber:application of an artificial neural network using non-destructive testing. Computer and Structure, 2009. 87, 1360- 1365.
	Fu Z Y , Zhao J Y , Huan S Q , et al. The variation of tangential rheological properties caused by shrinkage anisotropy and moisture content gradient in white birch disks. Holzforschung, 2015. 695, 573- 579.
	Hagan M T , Demuth H B , Beale M H . Neural network design. Boston: PWS Publishing Company. 1996.
	Iliadis L , Mansfield S D , Avramidis S , et al. Predicting Douglas-fir wood density by artificial neural networks ANN based on progeny testing information. Holzforschung, 2013. 677, 771- 777.
	Mansfield S D , Iliadis L , Avramidis S . Neural network prediction of bending strength and stiffness in western hemlock Tsuga heterophylla Raf. Holzforschung, 2007. 616, 707- 716.
	Myhara R M , Sablani S . Unification of fruit water sorption isotherm using artificial neural networks. Drying Technology, 2001. 198, 1543- 1554.
	Pang S . Modelling of stress development during drying and relief during steaming in Pinus radiata lumber. Drying Technology, 2000. 188, 1677- 1696.
	Sarle W S . Stopped training and other remedies for overfitting. Proceedings of the 27^th Symposium on the Interface of Computing Science and Statistics, 1995. 352-360
	Tiryaki S , Hamzacebi C . Predicting modulus of rupture MOR and modulus of elasticity MOE of heat treated woods by artificial neural networks. Measurement, 2014a. 49, 266- 274. doi: 10.1016/j.measurement.2013.12.004
	Tiryaki S , Aydın A . An artificial neural network model for predicting compression strength of heat treated woods and comparison with a multiple linear regression model. Construction and Building Materials, 2014b. 62, 102- 108. doi: 10.1016/j.conbuildmat.2014.03.041
	Watanabe K , Matsushita Y , Kobayashi I , et al. Artificial neural network modeling for predicting final moisture content of individual Sugi Cryptomeria japonica samples during air-drying. Journal of Wood Science, 2013. 59, 112- 118. doi: 10.1007/s10086-012-1314-2
	Watanabe K , Kobayashi I , Matsushita Y , et al. Application of near-infrared spectroscopy for evaluation of drying stress on lumber surface:a comparison of artificial neural networks and partial least squares regression. Drying Technology, 2014. 325, 590- 596.
	Zhan J F , Avramidis S . Mechano-sorptive creep of hemlock under conventional drying:ii. description of actual creep behavior in drying lumber. Drying Technology, 2011. 2910, 1140- 1149.
	Zhang D Y , Sun L , Cao J . Modeling of temperature-humidity for wood drying based on time-delay neural network. Journal of Forestry Research, 2006. 172, 141- 144.
	Zhang D Y , Liu Y X , Cao J , et al. Neural network prediction model of wood moisture content for drying process. Scientia Silvae Sinicae, 2008. 4412, 94- 98.

网络训练误差 Network training error	神经元数量Number of neurons
网络训练误差 Network training error	3	4	5	6	7	8	9
M1	2.83×10^-6	1.97×10^-6	1.56×10^-6	1.21×10^-6	9.67×10^-7	—	—
M2	3.56×10^-6	2.71×10^-6	2.23×10^-6	1.16×10^-6	1.87×10^-6	1.12×10^-6	9.20×10^-7

组别Groups	线性回归方程Linear regression equation	R	R²
训练集Training set	Pre=0.98Exp-7.7e-05	0.988	0.97
验证集Validation set	Pre=0.95Exp-0.000 31	0.983	0.96
测试集Test set	Pre=1Exp+0.000 1	0.978	0.95
总数据集All data set	Pre=0.98Exp-9.1e-05	0.985	0.97

[1]	Zhou Shiyu, Du Guangyue, Chu Xin, Liu Xiaoping, Zhou Yucheng. Prediction of Thermal Released Field by the Wood Flooring for Ground with Heating System Based on BP Network [J]. Scientia Silvae Sinicae, 2018, 54(11): 158-163.
[2]	Peng Hui, Jiang Jiali, Zhan Tianyi, Lü Jianxiong. A Review of Pure Viscoelastic Creep and Mechano-Sorptive Creep of Wood [J]. Scientia Silvae Sinicae, 2016, 52(4): 116-126.
[3]	Wang Qiang;Hu Haiqing. Estimation of Forest Fuel Load Based with Ridge Regression and Artificial Neural Networks [J]. Scientia Silvae Sinicae, 2012, 48(9): 108-114.
[4]	Yang Guangbin;Liu Pengju;Tang Xiaoming. Application of Automatic Selection System of Forest Fire Spread Models Driven by Dynamic Data [J]. Scientia Silvae Sinicae, 2011, 47(1): 107-112.
[5]	Yu Huaqiang;Zhao Rongjun;Liu Xing'e;Fei Benhua. A Review of Models of Creep in Wood [J]. Scientia Silvae Sinicae, 2007, 43(7): 101-105.
[6]	Hu Lin;Feng Zhongke;Nie Yuzao. Forest Fire Prediction Research Based on VLBP Neural Network [J]. , 2006, 42(zk): 155-158.
[7]	Qi Wei;Wang Lihai. Identifying the Patterns of Defects in Timber Using Ultrasonic Test Based on Wavelet Neural Networks [J]. Scientia Silvae Sinicae, 2006, 42(8): 63-68.
[8]	Wang Lihai;Zhao Zhengyong. Automatically Classifying and Identifying the TM Remote Sensing Images of Forest Mixed with Conifer and Broadleaves Using Improved BP ANN [J]. Scientia Silvae Sinicae, 2005, 41(6): 94-100.
[9]	Yang Jingbiao;Ma Xiaoqian. On the Basis of Artificial Neural Network to Forecast the Forest Fire in Guangdong Province [J]. Scientia Silvae Sinicae, 2005, 41(4): 127-132.
[10]	Yang Wenbin;Liu Yixing;Liu Yingtao. The Application Prospects of Artificial Neural Network in the Wood Industries [J]. Scientia Silvae Sinicae, 2004, 40(6): 153-157.
[11]	Zhan Jianfeng;Gu Jiyou;Ai Muye. The Preliminary Study on Drying Process Traverse Strains of Asian White Birch [J]. Scientia Silvae Sinicae, 2004, 40(5): 174-179.
[12]	He Dongjin;Hong Wei;Wu Chengzhen. A STUDY ON SIMULATING PREDICTIVE MODEL OF MEAN DBH FOR BAMBOO STANDS [J]. Scientia Silvae Sinicae, 2000, 36(zk): 148-153.
[13]	Li Dagang;Gu Lianbai. STUDY ON RHEOLOGICAL BEHAVIOR OF SURFACE LAYER OF POPLAR DURING HIGH-TEMPERATURE DRYING [J]. , 1999, 35(1): 83-89.

Simulation of Drying Strain Based on Artificial Neural Network Model

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 11

References 0

Related Articles 13

Recommended Articles

Metrics

Comments