Non-destructive assessment of moisture content and modulus of rupture of sawn timber Hevea wood using near infrared spectroscopy technique

Ruangkhasap, Sutida; Noypitak, Sirinad; Noknoi, W.; Terdwongworakul, Anupun

doi:10.1088/1757-899x/773/1/012065

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…Near-infrared spectroscopy (NIR) distinguishes between the properties of the wood surface, the chemical composition of the sample, the physical properties in the wood and the moisture content by an infrared signal [52]. In contrast to mass spectroscopy, grinding of the sample, repetition of the spectral analysis and data cleaning by means of calibrations and the deletion of outliers are not necessary.…”

Section: Passive Samplingmentioning

confidence: 99%

Systematics of Forestry Technology for Tracing the Timber Supply Chain

Kaulen,

Stopfer,

Lippert

et al. 2023

Forests

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Traceability is the ability to follow the processes that a raw material or product goes through. For forestry, this means identifying the wood from the standing tree to the mill entrance and recording all information about the technical (production) and spatial (transportation) manipulation of the timber by linking it to the ID. We reviewed the literature for developments in timber flow traceability. Findings range from disillusionment with the non-application of available forestry technology to enthusiasm for the advancement of technology that—given appropriate incentives of an economic, environmental, consumer-oriented and legislative nature—can rapidly lead to widespread end-to-end media-interruption-free implementation. Based on our research, the solution lies in optical biometric systems that identify the individual piece of wood—without attaching anything—at three crucial points: during assortment at the skid road, at the forest road and at the mill entrance. At all of these points, the data accruing during the timber supply process must be linked to the ID of the piece of wood via data management.

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Section: Passive Samplingmentioning

confidence: 99%

Systematics of Forestry Technology for Tracing the Timber Supply Chain

Kaulen,

Stopfer,

Lippert

et al. 2023

Forests

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“…Moisture content (MC) is the key factor of the physical and mechanical properties of timber. Below the fibre saturation point, different MC could lead to both strength and stiffness changes [1,2]. Also, MC affects wood decay, fungal growth, weight gain, which could considerably reduce the safety and durability of timber structures [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Timber moisture detection using wavelet packet decomposition and convolutional neural network

Yuan

Zhang

Chen

et al. 2021

Smart Mater. Struct.

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As timber structures are vulnerable to degradation due to the tendency to trap moisture, the present study proposed a new percussion-based method to replace the existing constant contact between structures and sensors. A total of two approaches have been proposed to automated detect the moisture content (MC) of timber: (a) the random forest classifier (machine learning-based) was employed to classify the wavelet packet decomposition (WPD) features extracted from excitation-induced sound signals (WPD + RF); and (b) the 2D-CNN framework (deep learning-based) was employed to classify the Mel frequency cepstral coefficient (MFCC) features extracted from excitation-induced sound signals (MFCC + 2DCNN). The proposed automatic detection methods are covered from 1D time-domain signal classification to 2D image classification. To verify the effectiveness of both two approaches, an experimental study was conducted. The MC of two types of timber specimens (i.e. softwood and hardwood) was gradually increased from 0% to 60% with 10% increments. The change of MC of timber material caused different material properties, resulting in a measurable differential in forced vibration among the various specimens used. The results demonstrated that MFCC + 2DCC outperformed the RF + WPD in MC classification of timber material. Overall, the percussion-based method proposed in this study can provide an outstanding classification performance.

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