Acoustic emission monitoring of wood materials and timber structures: A critical review

Nasir, Vahid; Ayanleye, Samuel; Kazemirad, Siavash; Sassani, Farrokh; Αδαμόπουλος, Στέργιος

doi:10.1016/j.conbuildmat.2022.128877

Cited by 41 publications

(14 citation statements)

References 125 publications

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“…Finally, future research should investigate the effectiveness of different NDE methods for the fast and reliable assessment of timber MC. Acoustic and ultrasound signals were shown to be sensitive to wood characteristics such as MC [ 79 ]. Additionally, the suitability of near-infrared (NIR) spectroscopy, as a widely used NDE method for wood characterization and monitoring [ 80 , 81 , 82 , 83 ], could be assessed for MC monitoring in kiln-dried timber at sawmills.…”

Section: Resultsmentioning

confidence: 99%

The Role of Drying Schedule and Conditioning in Moisture Uniformity in Wood: A Machine Learning Approach

et al. 2023

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Monitoring the moisture content (MC) of wood and avoiding large MC variation is a crucial task as a large moisture spread after drying significantly devalues the product, especially in species with high green MC spread. Therefore, this research aims to optimize kiln-drying and provides a predictive approach to estimate and classify target timber moisture, using a gradient-boosting machine learning model. Inputs include three wood attributes (initial moisture, initial weight, and basic density) and three drying parameters (schedule, conditioning, and post-storage). Results show that initial weight has the highest correlation with the final moisture and possesses the highest relative importance in both predictive and classifier models. This model demonstrated a drop in training accuracy after removing schedule, conditioning, and post-storage from inputs, emphasizing that the drying parameters are significant in the robustness of the model. However, the regression-based model failed to satisfactorily predict the moisture after kiln-drying. In contrast, the classifying model is capable of classifying dried wood into acceptable, over-, and under-dried groups, which could apply to timber pre- and post-sorting. Overall, the gradient-boosting model successfully classified the moisture in kiln-dried western hemlock timber.

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Section: Resultsmentioning

confidence: 99%

The Role of Drying Schedule and Conditioning in Moisture Uniformity in Wood: A Machine Learning Approach

et al. 2023

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“…The industrial scale characterization and quality control of wood and wood-based materials require the development of fast and reliable non-destructive evaluation (NDE) tools. Near-infrared (NIR) spectroscopy (wavelengths ranging from 800 to 2500 nm) is one of many NDE methods, and it has been widely investigated for wood-quality control and characterization purposes [ 1 , 2 ]. A variety of wood-related applications have been explored and include the prediction of chemical, physical, and mechanical properties, the classification and identification of wood species, and the performance of wood materials and timber structures under weathering and photodegradation [ 3 , 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Utilization of Synthetic Near-Infrared Spectra via Generative Adversarial Network to Improve Wood Stiffness Prediction

Ali,

Raut,

Dahlen

et al. 2024

Sensors

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Near-infrared (NIR) spectroscopy is widely used as a nondestructive evaluation (NDE) tool for predicting wood properties. When deploying NIR models, one faces challenges in ensuring representative training data, which large datasets can mitigate but often at a significant cost. Machine learning and deep learning NIR models are at an even greater disadvantage because they typically require higher sample sizes for training. In this study, NIR spectra were collected to predict the modulus of elasticity (MOE) of southern pine lumber (training set = 573 samples, testing set = 145 samples). To account for the limited size of the training data, this study employed a generative adversarial network (GAN) to generate synthetic NIR spectra. The training dataset was fed into a GAN to generate 313, 573, and 1000 synthetic spectra. The original and enhanced datasets were used to train artificial neural networks (ANNs), convolutional neural networks (CNNs), and light gradient boosting machines (LGBMs) for MOE prediction. Overall, results showed that data augmentation using GAN improved the coefficient of determination (R2) by up to 7.02% and reduced the error of predictions by up to 4.29%. ANNs and CNNs benefited more from synthetic spectra than LGBMs, which only yielded slight improvement. All models showed optimal performance when 313 synthetic spectra were added to the original training data; further additions did not improve model performance because the quality of the datapoints generated by GAN beyond a certain threshold is poor, and one of the main reasons for this can be the size of the initial training data fed into the GAN. LGBMs showed superior performances than ANNs and CNNs on both the original and enhanced training datasets, which highlights the significance of selecting an appropriate machine learning or deep learning model for NIR spectral-data analysis. The results highlighted the positive impact of GAN on the predictive performance of models utilizing NIR spectroscopy as an NDE technique and monitoring tool for wood mechanical-property evaluation. Further studies should investigate the impact of the initial size of training data, the optimal number of generated synthetic spectra, and machine learning or deep learning models that could benefit more from data augmentation using GANs.

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“…Li M et al [ 12 ] investigated the propagation characteristics of AE signals along the wood grain direction based on discrete wavelet analysis and established an energy attenuation model for AE signals, which showed that the energy of AE signals decayed exponentially with the propagation distance when the surface transverse wave and the internal longitudinal wave propagated through the wood. Nasir V et al [ 13 ] systematically reviewed the AE method and its application to the wood and timber industry and discussed the effect of wood properties on the velocity and energy attenuation of the AE signal. Diakhate M et al [ 14 ] compared cluster analysis of AE data with numerical modeling for the assessment of crack length evolution, and the results demonstrated a good correlation between the AE results and numerical predictions.…”

Section: Introductionmentioning

confidence: 99%

Analysis of critical states based on acoustic emission signals during progressive failure of wood

Jia,

LI,

Zhang

et al. 2024

PLoS ONE

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The analysis of critical states during fracture of wood materials is crucial for wood building safety monitoring, wood processing, etc. In this paper, beech and camphor pine are selected as the research objects, and the acoustic emission signals during the fracture process of the specimens are analyzed by three-point bending load experiments. On the one hand, the critical state interval of a complex acoustic emission signal system is determined by selecting characteristic parameters in the natural time domain. On the other hand, an improved method of b_value analysis in the natural time domain is proposed based on the characteristics of the acoustic emission signal. The K-value, which represents the beginning of the critical state of a complex acoustic emission signal system, is further defined by the improved method of b_value in the natural time domain. For beech, the analysis of critical state time based on characteristic parameters can predict the “collapse” time 8.01 s in advance, while for camphor pines, 3.74 s in advance. K-value can be analyzed at least 3 s in advance of the system “crash” time for beech and 4 s in advance of the system “crash” time for camphor pine. The results show that compared with traditional time-domain acoustic emission signal analysis, natural time-domain acoustic emission signal analysis can discover more available feature information to characterize the state of the signal. Both the characteristic parameters and Natural_Time_b_value analysis in the natural time domain can effectively characterize the time when the complex acoustic emission signal system enters the critical state. Critical state analysis can provide new ideas for wood health monitoring and complex signal processing, etc.

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Acoustic emission monitoring of wood materials and timber structures: A critical review

Cited by 41 publications

References 125 publications

The Role of Drying Schedule and Conditioning in Moisture Uniformity in Wood: A Machine Learning Approach

The Role of Drying Schedule and Conditioning in Moisture Uniformity in Wood: A Machine Learning Approach

Utilization of Synthetic Near-Infrared Spectra via Generative Adversarial Network to Improve Wood Stiffness Prediction

Analysis of critical states based on acoustic emission signals during progressive failure of wood

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