Predicting modulus of rupture (MOR) and modulus of elasticity (MOE) of heat treated woods by artificial neural networks

Tiryaki, Sebahattin; Hamzaçebi, Çoşkun

doi:10.1016/j.measurement.2013.12.004

Cited by 66 publications

(39 citation statements)

References 29 publications

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“…), modulus of elasticity (MOE) and modulus of rupture (MOR). The correlation coefficient of artificial neural network found in this work was higher than others reported for wood properties using this technique in literature, as fracture toughness in solid wood, 0.62 (SAMARASINGHE et al, 2007); modulus of elasticity and rupture in structural panels, 0.73 and 0.66 respectively (FERNANDEZ et al, 2012) and similar to those of heat treated wood properties of Fagus orientalis and Picea orientalis, above 0.99 (TIRYAKI et al, 2014b).…”

Section: Resultscontrasting

confidence: 53%

Prediction of the physical, mechanical and colorimetric properties of Eucalyptus grandis heat-treated wood using artificial neural networks

Zanuncio¹,

Carvalho²,

Silva³

et al. 2017

Sci. For.

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Section: Resultscontrasting

confidence: 53%

Prediction of the physical, mechanical and colorimetric properties of Eucalyptus grandis heat-treated wood using artificial neural networks

Zanuncio¹,

Carvalho²,

Silva³

et al. 2017

Sci. For.

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“…Figure 2 shows the representations of the optimal architectures of the ANN models for predicting volumetric swelling and shrinkage of heat treated pine and beech. Regarding the neurons of layers, it was previously reported that the number of input and output neurons corresponds to the number of input and output variables, respectively (Tiryaki and Hamzacebi 2014). Hence, the inputs of both models for the present study were wood species, treatment temperature and exposure time, while the outputs of the models were volumetric swelling and shrinkage.…”

Section: Model Architecturesmentioning

confidence: 83%

“…Among the evaluation criteria, the criterion of MAPE, which corresponds to the average of the errors in Table 2, is mostly accepted as the main criterion in making a decision on the performance of a model (Tiryaki and Hamzacebi 2014). The MAPE shows the average of the percentage deviation from the targeted value.…”

Section: Modeling Resultsmentioning

confidence: 99%

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Analysis of volumetric swelling and shrinkage of heat treated woods: Experimental and artificial neural network modeling approach

Tiryaki

Bardak

Aydın

et al. 2016

Maderas, Cienc. tecnol.

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Shrinkage and swelling characteristics of wood as a hygroscopic material affect negatively its effective utilization for a variety of applications. Heat treatment is widely used for minimizing the negative effects of volumetric swelling and shrinkage of wood. The present study aims to develop artificial neural network (ANN) models for predicting volumetric swelling and shrinkage of heat treated woods. For this purpose, wood samples were subjected to heat treatment at varying temperatures (130, 150, 170 and 190 ºC) for varying durations (2, 4, 6 and 8 h). Experimental results have showed that volumetric swelling and shrinkage of wood decreased by heat treatment. Then, neural networks models capable of predicting the swelling and shrinkage of the treated woods were developed based on the resulting data. It was seen that ANN models allowed volumetric swelling and shrinkage of such woods to predict successfully with a limited set of experimental data. This approach was able to predict volumetric swelling and shrinkage of wood with a mean absolute percentage error equal to 2,599% and 2,647% in test phase, respectively. The developed models might thus serve as a robust tool to predict volumetric swelling and shrinkage with less number of experiments.

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“…[15][16][17]. In this paper, we aimed to determine the wood density based on intensity of RGB color on wood surface, develop the calibration equation using color values and evaluate the calibration efficiency by prediction the wood density using Fuzzy logic.…”

Section: Figure 1 Fuzzy Inference Systemmentioning

confidence: 99%

Prediction of Wood Density by Using Red-Green-Blue (RGB) Color and Fuzzy Logic Techniques

Bardak¹,

Bardak²

2017

Journal of Polytechnic

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Prediction of wood density by using redgreen-blue (rgb) color and fuzzy logic techniques Kırmızı-yeşil-mavi (kym) renk ve bulanık mantık teknikleri kullanılarak odun yoğunluğu tahmini Yazar(lar) (Author(s)): Timuçin BARDAK, Selahattin BARDAKBu makaleye şu şekilde atıfta bulunabilirsiniz(To cite to this article): Bardak T., and Bardak S., "Prediction of wood density by using red-green-blue (rgb) color and fuzzy logic techniques", Politeknik Dergisi, 20(4): 979-984, (2017). Politeknik Dergisi, 2017; 20 (4) : 979-984 Journal of Polytechnic, 2017; 20 (4) : 979-984 ABSTRACTDensity is an important wood property since it correlates to mechanical properties of wood. Fuzzy logic, among the various available Artificial Intelligence techniques, emerges as a good technique in predicting. Digital image analysis is an powerful tool to obtain meaningful data out of an image. In this study, digital image processing based on a red-green-blue (RGB) color examination was practiced to measure the intensity of wood color. Densities of the test samples were measured. Then, a new fuzzy logic model was developed based on these measured values and RGB color intensity of wood. Afterwards, the experimental and modeling data results were compared. 98.17% accuracy was observed between the measurement and the fuzzy logic model. Consequently, Fuzzy logic is visable method for the prediction of the wood density. Keywords

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Predicting modulus of rupture (MOR) and modulus of elasticity (MOE) of heat treated woods by artificial neural networks

Cited by 66 publications

References 29 publications

Prediction of the physical, mechanical and colorimetric properties of Eucalyptus grandis heat-treated wood using artificial neural networks

Prediction of the physical, mechanical and colorimetric properties of Eucalyptus grandis heat-treated wood using artificial neural networks

Analysis of volumetric swelling and shrinkage of heat treated woods: Experimental and artificial neural network modeling approach

Prediction of Wood Density by Using Red-Green-Blue (RGB) Color and Fuzzy Logic Techniques

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