Effect of wood surface roughness on prediction of structural timber properties by infrared spectroscopy using ANFIS, ANN and PLS regression

Ayanleye, Samuel; Nasir, Vahid; Avramidis, Stavros; Cool, Julie

doi:10.1007/s00107-020-01621-x

Cited by 36 publications

(17 citation statements)

References 68 publications

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“…1,2 These methods are costly and time-consuming and cannot be used for in situ nondestructive characterization of the elastic properties of wood. The near-infrared (NIR) spectroscopy [3][4][5] and the ultrasonic and stress wave propagation methods [6][7][8][9][10][11] are the main nondestructive evaluation methods used in prior research studies for the characterization of the modulus of elasticity (MOE) of wood. The main characterization approach of the wave-based methods is to estimate the MOE from the dynamic MOE obtained from the measured ultrasonic wave velocity.…”

Section: Introductionmentioning

confidence: 99%

Mechanical degradation of wood under ultraviolet radiation characterized by Lamb wave propagation

Fathi

Kazemirad

Nasir

2021

Struct Control Health Monit

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Nondestructive characterization of wood exposed to ultraviolet (UV) radiation is a critical task for health monitoring purposes since photodegradation affects the mechanical properties of materials. The main purpose of this study was to assess the effect of UV radiation on the elastic and viscoelastic properties of fir, poplar, oak, and alder wood using the Lamb wave propagation method. The Lamb wave propagation and mechanical three-point bending tests were performed on the wood specimens exposed to UV radiation for different time periods. The modulus of elasticity (MOE) and the viscoelastic properties of the specimens including the storage and loss moduli and the loss factor were estimated using the experimentally measured wave characteristics. The difference between the MOE estimated from the Lamb wave propagation and three-point bending tests was less than 4%, proving the capability of the Lamb wave propagation method for accurate nondestructive and in situ assessment of wood. It was revealed that the UV radiation led to a decrease in the MOE and storage modulus of wood specimens and an increase in their loss factor, resulting in lower stiffness and higher viscoelasticity of wood. It was also shown that the sensitivity of the loss factor to UV radiation was higher than that of the MOE and storage modulus, emphasizing the importance of the viscoelasticity assessment of wood products under photodegradation, weathering, and extreme radiation.

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Section: Introductionmentioning

confidence: 99%

Mechanical degradation of wood under ultraviolet radiation characterized by Lamb wave propagation

Fathi

Kazemirad

Nasir

2021

Struct Control Health Monit

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“…Near-infrared (NIR) spectroscopy is one of the most commonly used NDE methods for wood characterization. NIR spectroscopy is sensitive to changes in the chemical composition of wood [ 5 ] and has been used for wood classification and the estimation of different wood properties [ 6 , 7 , 8 , 9 , 10 ]. Wave propagation-based methods have also been employed to estimate timber MOE [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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 R2 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.

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“…The mechanical properties of wood have been widely characterized by destructive tensile and bending tests, 14,15 which are not suitable for in-situ monitoring. The most popular methods used in the literature for nondestructive evaluation (NDE) of mechanical properties of wood are the near-infrared (NIR) spectroscopy, 16,17 and the ultrasonic and stress wave and ultrasonic wave propagation methods. [18][19][20][21][22][23][24][25][26][27] These methods have been mainly employed to characterize and predict the MOE of wood materials.…”

Section: Introductionmentioning

confidence: 99%

“…Figure16. The relative importance of parameters for predicting the (a) MOE and (b) MOR of the wood specimens using all of the input features.…”

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Combined machine learning–wave propagation approach for monitoring timber mechanical properties under UV aging

Nasir

Fathi

Kazemirad

2021

Structural Health Monitoring

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This study proposes a combined machine learning–wave propagation approach for nondestructive prediction of the modulus of elasticity (MOE) and rupture (MOR) of timber subjected to ultraviolet (UV) radiation. Fir, poplar, alder, and oak wood specimens were subjected to artificial UV aging and assessed using the Lamb wave propagation. Different features including the wave characteristics and the viscoelastic properties of the specimens were obtained from the Lamb wave propagation tests. The extracted features trained a decision tree model for MOE and MOR prediction. The UV radiation caused a decrease in the elastic properties of wood but increased its viscoelasticity. The results also showed a decrease in the wave velocity and an increase in the wave amplitude decay with the UV exposure time. It was revealed that compared with the wave velocity, the wave amplitude decay was better correlated to the MOE of MOR of UV-degraded wood. The MOE and MOR of UV-degraded wood were accurately predicted by the machine learning models fed by the features extracted from the Lamb wave propagation tests, where the shear storage modulus was found as the most important feature for training the models. It was concluded that the proposed approach offers a great tool for in-situ monitoring of wood structures under weathering and photodegradation conditions.

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Effect of wood surface roughness on prediction of structural timber properties by infrared spectroscopy using ANFIS, ANN and PLS regression

Cited by 36 publications

References 68 publications

Mechanical degradation of wood under ultraviolet radiation characterized by Lamb wave propagation

Mechanical degradation of wood under ultraviolet radiation characterized by Lamb wave propagation

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

Combined machine learning–wave propagation approach for monitoring timber mechanical properties under UV aging

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