Prediction of Mechanical Properties of Artificially Weathered Wood by Color Change and Machine Learning

Color parameters were used in this study to develop a machine learning model for predicting the mechanical properties of artificially weathered fir, alder, oak, and poplar wood. A CIELAB color measuring system was employed to study the color changes in wood samples. The color parameters were fed int...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Vahid Nasir, Hamidreza Fathi, Arezoo Fallah, Siavash Kazemirad, Farrokh Sassani, Petar Antov
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	wood characterization mechanical properties photodegradation artificial weathering color change ultraviolet radiation Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
Acceso en línea:	https://doaj.org/article/2313d42527f64906835bc31d1e788954
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:2313d42527f64906835bc31d1e788954
record_format	dspace
spelling	oai:doaj.org-article:2313d42527f64906835bc31d1e7889542021-11-11T17:53:10ZPrediction of Mechanical Properties of Artificially Weathered Wood by Color Change and Machine Learning10.3390/ma142163141996-1944https://doaj.org/article/2313d42527f64906835bc31d1e7889542021-10-01T00:00:00Zhttps://www.mdpi.com/1996-1944/14/21/6314https://doaj.org/toc/1996-1944Color parameters were used in this study to develop a machine learning model for predicting the mechanical properties of artificially weathered fir, alder, oak, and poplar wood. A CIELAB color measuring system was employed to study the color changes in wood samples. The color parameters were fed into a decision tree model for predicting the MOE and MOR values of the wood samples. The results indicated a reduction in the mechanical properties of the samples, where fir and alder were the most and least degraded wood under weathering conditions, respectively. The mechanical degradation was correlated with the color change, where the most resistant wood to color change exhibited less reduction in the mechanical properties. The predictive machine learning model estimated the MOE and MOR values with a maximum R<sup>2</sup> of 0.87 and 0.88, respectively. Thus, variations in the color parameters of wood can be considered informative features linked to the mechanical properties of small-sized and clear wood. Further research could study the effectiveness of the model when analyzing large-sized timber.Vahid NasirHamidreza FathiArezoo FallahSiavash KazemiradFarrokh SassaniPetar AntovMDPI AGarticlewood characterizationmechanical propertiesphotodegradationartificial weatheringcolor changeultraviolet radiationTechnologyTElectrical engineering. Electronics. Nuclear engineeringTK1-9971Engineering (General). Civil engineering (General)TA1-2040MicroscopyQH201-278.5Descriptive and experimental mechanicsQC120-168.85ENMaterials, Vol 14, Iss 6314, p 6314 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	wood characterization mechanical properties photodegradation artificial weathering color change ultraviolet radiation Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
spellingShingle	wood characterization mechanical properties photodegradation artificial weathering color change ultraviolet radiation Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85 Vahid Nasir Hamidreza Fathi Arezoo Fallah Siavash Kazemirad Farrokh Sassani Petar Antov Prediction of Mechanical Properties of Artificially Weathered Wood by Color Change and Machine Learning
description	Color parameters were used in this study to develop a machine learning model for predicting the mechanical properties of artificially weathered fir, alder, oak, and poplar wood. A CIELAB color measuring system was employed to study the color changes in wood samples. The color parameters were fed into a decision tree model for predicting the MOE and MOR values of the wood samples. The results indicated a reduction in the mechanical properties of the samples, where fir and alder were the most and least degraded wood under weathering conditions, respectively. The mechanical degradation was correlated with the color change, where the most resistant wood to color change exhibited less reduction in the mechanical properties. The predictive machine learning model estimated the MOE and MOR values with a maximum R<sup>2</sup> of 0.87 and 0.88, respectively. Thus, variations in the color parameters of wood can be considered informative features linked to the mechanical properties of small-sized and clear wood. Further research could study the effectiveness of the model when analyzing large-sized timber.
format	article
author	Vahid Nasir Hamidreza Fathi Arezoo Fallah Siavash Kazemirad Farrokh Sassani Petar Antov
author_facet	Vahid Nasir Hamidreza Fathi Arezoo Fallah Siavash Kazemirad Farrokh Sassani Petar Antov
author_sort	Vahid Nasir
title	Prediction of Mechanical Properties of Artificially Weathered Wood by Color Change and Machine Learning
title_short	Prediction of Mechanical Properties of Artificially Weathered Wood by Color Change and Machine Learning
title_full	Prediction of Mechanical Properties of Artificially Weathered Wood by Color Change and Machine Learning
title_fullStr	Prediction of Mechanical Properties of Artificially Weathered Wood by Color Change and Machine Learning
title_full_unstemmed	Prediction of Mechanical Properties of Artificially Weathered Wood by Color Change and Machine Learning
title_sort	prediction of mechanical properties of artificially weathered wood by color change and machine learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2313d42527f64906835bc31d1e788954
work_keys_str_mv	AT vahidnasir predictionofmechanicalpropertiesofartificiallyweatheredwoodbycolorchangeandmachinelearning AT hamidrezafathi predictionofmechanicalpropertiesofartificiallyweatheredwoodbycolorchangeandmachinelearning AT arezoofallah predictionofmechanicalpropertiesofartificiallyweatheredwoodbycolorchangeandmachinelearning AT siavashkazemirad predictionofmechanicalpropertiesofartificiallyweatheredwoodbycolorchangeandmachinelearning AT farrokhsassani predictionofmechanicalpropertiesofartificiallyweatheredwoodbycolorchangeandmachinelearning AT petarantov predictionofmechanicalpropertiesofartificiallyweatheredwoodbycolorchangeandmachinelearning
_version_	1718431991351738368

Prediction of Mechanical Properties of Artificially Weathered Wood by Color Change and Machine Learning

Ejemplares similares