Nondestructive Testing of Mechanical Properties of Bamboo–Wood Composite Container Floor by Image Processing

The bamboo–wood composite container floor (BWCCF) has been wildly utilized in transportation in recent years. However, most of the common approaches of mechanics detection are conducted in a time-consuming and resource wasting way. Therefore, this paper aims to provide a frugal and highly efficient...

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Autores principales:	Zhilin Jiang, Yi Liang, Zihua Su, Aonan Chen, Jianping Sun
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	bamboo–wood composite container floor mechanical property image processing artificial neural network support vector machine Plant ecology QK900-989
Acceso en línea:	https://doaj.org/article/2e6eb217b76745c9b2a280eb3b0f82d9
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spelling	oai:doaj.org-article:2e6eb217b76745c9b2a280eb3b0f82d92021-11-25T17:38:22ZNondestructive Testing of Mechanical Properties of Bamboo–Wood Composite Container Floor by Image Processing10.3390/f121115351999-4907https://doaj.org/article/2e6eb217b76745c9b2a280eb3b0f82d92021-11-01T00:00:00Zhttps://www.mdpi.com/1999-4907/12/11/1535https://doaj.org/toc/1999-4907The bamboo–wood composite container floor (BWCCF) has been wildly utilized in transportation in recent years. However, most of the common approaches of mechanics detection are conducted in a time-consuming and resource wasting way. Therefore, this paper aims to provide a frugal and highly efficient method to predict the short-span shear stress, the modulus of rupture (MOR) and the modulus of elasticity (MOE) of the BWCCF. Artificial neural network (ANN) models were developed and support vector machine (SVM) models were constructed for comparative study by taking the characteristic parameters of image processing as input and the mechanical properties as output. The results show that the SVM models can output better values than the ANN models. In a prediction of the three mechanical properties by SVMs, the correlation coefficients (R) were determined as 0.899, 0.926, and 0.949, and the mean absolute percentage errors (MAPE) were obtained, 6.983%, 5.873%, and 4.474%, respectively. The performance measures show the strong generalization of the SVM models. The discoveries in this work provide new perspectives on the study of mechanical properties of the BWCCF combining machine learning and image processing.Zhilin JiangYi LiangZihua SuAonan ChenJianping SunMDPI AGarticlebamboo–wood composite container floormechanical propertyimage processingartificial neural networksupport vector machinePlant ecologyQK900-989ENForests, Vol 12, Iss 1535, p 1535 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	bamboo–wood composite container floor mechanical property image processing artificial neural network support vector machine Plant ecology QK900-989
spellingShingle	bamboo–wood composite container floor mechanical property image processing artificial neural network support vector machine Plant ecology QK900-989 Zhilin Jiang Yi Liang Zihua Su Aonan Chen Jianping Sun Nondestructive Testing of Mechanical Properties of Bamboo–Wood Composite Container Floor by Image Processing
description	The bamboo–wood composite container floor (BWCCF) has been wildly utilized in transportation in recent years. However, most of the common approaches of mechanics detection are conducted in a time-consuming and resource wasting way. Therefore, this paper aims to provide a frugal and highly efficient method to predict the short-span shear stress, the modulus of rupture (MOR) and the modulus of elasticity (MOE) of the BWCCF. Artificial neural network (ANN) models were developed and support vector machine (SVM) models were constructed for comparative study by taking the characteristic parameters of image processing as input and the mechanical properties as output. The results show that the SVM models can output better values than the ANN models. In a prediction of the three mechanical properties by SVMs, the correlation coefficients (R) were determined as 0.899, 0.926, and 0.949, and the mean absolute percentage errors (MAPE) were obtained, 6.983%, 5.873%, and 4.474%, respectively. The performance measures show the strong generalization of the SVM models. The discoveries in this work provide new perspectives on the study of mechanical properties of the BWCCF combining machine learning and image processing.
format	article
author	Zhilin Jiang Yi Liang Zihua Su Aonan Chen Jianping Sun
author_facet	Zhilin Jiang Yi Liang Zihua Su Aonan Chen Jianping Sun
author_sort	Zhilin Jiang
title	Nondestructive Testing of Mechanical Properties of Bamboo–Wood Composite Container Floor by Image Processing
title_short	Nondestructive Testing of Mechanical Properties of Bamboo–Wood Composite Container Floor by Image Processing
title_full	Nondestructive Testing of Mechanical Properties of Bamboo–Wood Composite Container Floor by Image Processing
title_fullStr	Nondestructive Testing of Mechanical Properties of Bamboo–Wood Composite Container Floor by Image Processing
title_full_unstemmed	Nondestructive Testing of Mechanical Properties of Bamboo–Wood Composite Container Floor by Image Processing
title_sort	nondestructive testing of mechanical properties of bamboo–wood composite container floor by image processing
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2e6eb217b76745c9b2a280eb3b0f82d9
work_keys_str_mv	AT zhilinjiang nondestructivetestingofmechanicalpropertiesofbamboowoodcompositecontainerfloorbyimageprocessing AT yiliang nondestructivetestingofmechanicalpropertiesofbamboowoodcompositecontainerfloorbyimageprocessing AT zihuasu nondestructivetestingofmechanicalpropertiesofbamboowoodcompositecontainerfloorbyimageprocessing AT aonanchen nondestructivetestingofmechanicalpropertiesofbamboowoodcompositecontainerfloorbyimageprocessing AT jianpingsun nondestructivetestingofmechanicalpropertiesofbamboowoodcompositecontainerfloorbyimageprocessing
_version_	1718412156689448960

Nondestructive Testing of Mechanical Properties of Bamboo–Wood Composite Container Floor by Image Processing

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