Nodal defects and their influence on wave propagation in juvenile wood of Tectona grandis

Ferreira Rocha, H., Braga Mulin, L., Pollastrelli Rodrigues, B., Franca Dias, N. G., dos Santos Alves, N., Bertoldo Pedroso, C., Mantilla Carrasco, E. V., Ross, R. J., and Costa Alves, R. (2026). "Nodal defects and their influence on wave propagation in juvenile wood of Tectona grandis," BioResources 21(4), 9932–9946.

Abstract

Acoustics plays a significant role in the classification of a wide range of products in wood science and technology, from standing trees to structural elements. The study of wood acoustics involves the use of mechanical waves that are sensitive to several factors, including sample dimensions, intrinsic characteristics of the wood species, and the presence of defects. This study aimed to estimate the modulus of elasticity in juvenile wood of Tectona grandis using three different non-destructive wave-based evaluation methods: ultrasound, impulse vibration, and stress waves, with the goal of identifying the most suitable technique for predicting this mechanical property. Additionally, the study evaluated the impact of knots on wave propagation velocity and, consequently, on the estimation of the modulus of elasticity. Specimens measuring 20 × 20 × 300 mm (width, thickness, and length, respectively) were used, totaling 42 samples divided into two groups: with knots and without knots. The results indicated that all evaluated methods were effective and exhibited strong correlations with the estimated modulus of elasticity. Furthermore, the presence of knots in young, fast-growing teak wood did not significantly influence wave propagation.

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Nodal Defects and Their Influence on Wave Propagation in Juvenile Wood of Tectona grandis

Henzza Ferreira Rocha ,^a Lucas Braga Mulin ,^a Brunela Pollastrelli Rodrigues ,^b Nathália Gabriele Franca Dias ,^a Náthali dos Santos Alves ,^a Cinthya Bertoldo Pedroso ,^c Ana Paula Coelho Duarte ,ᵈ Edgar Vladimiro Mantilla Carrasco ,ᵉ Robert J. Ross, ^f and Rejane Costa Alves ^a,*

DOI: 10.15376/biores.21.4.9932-9946

Keywords: Non-destructive testing; Acoustic properties; Structural anisotropy; Internal irregularities; Teak

Contact information: a: Department of Forest Sciences and Wood Science, Center for Agricultural Sciences and Engineering (CCAE), Federal University of Espírito Santo (UFES), Alegre, ES, Brazil; b: Department of Forestry and Environmental Conservation, Clemson University, 307 Cherry Road, Clemson, SC 29634, USA; c: School of Agricultural Engineering, University of Campinas (UNICAMP), Cidade Universitária Zeferino Vaz, s/n, Barão Geraldo, 13083-875, Campinas, SP, Brazil, P.O. Box 6011; d: Universidad de la República, Department of Forest Production and Wood Technology, Uruguay; e: Federal University of Minas Gerais (UFMG), School of Architecture, Department of Technology, Rua Paraíba, 697, Funcionários, 30130-140, Belo Horizonte, MG, Brazil; f: Forest Products Laboratory, One Gifford Pinchot Drive, Madison, WI 53726-2398 USA;

*Corresponding author: [email protected]

Graphical Abstract

INTRODUCTION

Wood grading is a fundamental step in the valorization process, as it determines whether the material is suitable for structural applications (Burgers et al. 2019). This process is based on well-established characteristics that can be obtained through non-destructive measurements, enabling the identification of reliable indicators of mechanical performance. Consequently, there is a growing demand for efficient wood characterization methods and analytical approaches that contribute to reducing material losses.

Although technical standards describe procedures for evaluating the mechanical strength of wood through static tests, interest in dynamic methods that preserve sample integrity has increased in recent years (Fathi et al. 2020). This shift is driven by the need for industries to assess wood batches prior to commercialization. In this context, non-destructive testing methods have become essential tools, as they allow material characterization without damaging the specimens or compromising their final use, while also enabling greater efficiency in industrial-scale grading (Bresolin and Pravia 2021; Afoutou et al. 2024).

Wave propagation techniques have proven to be an effective approach for estimating wood properties and they are widely used in the industry to grade logs, lumber, and veneers (Ross 2015). Acoustic tomography and ultrasonic waves have also been applied to estimate wood stiffness constants, although the agreement with static measurements may vary according to the method and analytical approach employed (Alves et al. 2015). Studies conducted by Papandrea et al. (2022), Duong et al. (2022), Zhang et al. (2022), Silva et al. (2023), Carvalho et al. (2023), and Virgen-Cobos et al. (2022) applied wave-based tests to determine modulus of elasticity values. Accurate interpretation of these results requires understanding the factors that influence wave behavior in wood, which must be considered in the analyses (Bucur 1983).

Knots are natural features resulting from branch growth and insertion and are particularly common in fast-growing species (Ruan et al. 2024). Their influence on mechanical strength is associated with the proportion of the cross-sectional area they occupy, as well as their number, type, size, and distribution along the length and across the cross-section of the structural element (Barbosa et al. 2019). The nodal region is characterized by changes in fiber orientation, local density variations, and specific anatomical modifications, which may either generate stress concentrations or act as partial structural compensation mechanisms, thereby affecting the mechanical performance of wood (Lyu et al. 2023; Zhang et al. 2024). Thus, the way the knot connects to the surrounding tissues directly influences stress redistribution under static loading. Moreover, this interaction may alter material continuity for mechanical wave propagation, affecting dynamic parameters used in estimating the modulus of elasticity.

Tectona grandis plays a prominent role in the international market and is commonly cultivated in short-rotation plantations in Brazil. Rapid early growth in these systems promotes intense branching and a higher occurrence of knots in the first growth rings, resulting in wood with high structural heterogeneity (Ruan et al. 2024). Considering that a large portion of current teak production comes from young stands, it is essential to understand how these characteristics influence both mechanical behavior and the reliability of non-destructive methods applied to technological grading.

The present study aimed to evaluate whether the presence of knots influences mechanical wave propagation in juvenile wood of Tectona grandis, analyzing the potential effects on the dynamic parameters used to estimate mechanical properties.

Based on this context, we hypothesized that (i) all methods provide reliable estimates of the static modulus of elasticity, although with different predictive accuracies, (ii) the influence of knots on wave propagation differs among impulse excitation, stress wave, and ultrasonic testing methods, and (iii) the presence of knots affects mechanical wave propagation in juvenile wood of teak.

EXPERIMENTAL

Study Area and Material Collection

The study was conducted at the Laboratory of Wood Experimentation and Multiple Uses, which is part of the Department of Forest and Wood Sciences at the Federal University of Espírito Santo, Alegre, ES, Brazil. Tectona grandis wood from an 11-year-old plantation was used. Dynamic wave propagation tests and static bending tests were performed. The samples had dimensions of 20 × 20 × 300 mm (width, thickness, and length, respectively), in accordance with ISO 13061-3 (2014), totaling 42 specimens divided into two treatments: without knots (MSN) and with knots (MCN). In the without-knot group (MSN), the specimens were free from any visible defects. In the knot group (MCN), the specimens contained knots of varying sizes, ranging from small to large, distributed along the length of the pieces.

The moisture content and apparent density of the specimens were determined according to ASTM D4442-20 (2020) and ASTM D2395-17 (2017), respectively. Three non-destructive wave propagation methods were employed: vibration or impulse excitation, stress waves, and ultrasound.

Excitation Vibration

The specimens were subjected to the characterization of flexural and longitudinal vibration modes. An adjustable bar support, an impact hammer, a directional sensor, and dedicated software were used. Figure 1 illustrates the experimental setup during testing, showing the longitudinal and flexural configurations, respectively. The impulse excitation technique followed the procedures described in ASTM E1876 (2021).

The Sonelastic testing system was used to determine the elastic moduli based on the natural vibration frequencies of the specimens. The frequencies were excited by the impact hammer, and the acoustic response was captured by a sensor. The signal was then mathematically processed using a Fourier transform to obtain the corresponding frequency spectrum. Subsequently, the dynamic elastic moduli were calculated by considering the specimen geometry, mass, dimensions, and the frequencies obtained from the equipment, as presented in Eq. 1.

To determine the longitudinal modulus of elasticity (E), the vibration frequencies in the longitudinal (fl) and flexural (ff) directions were used in the calculations, following the standard procedures established by ASTM E1876 (2021),

where E_dyn is the modulus of elasticity (Pa) determined from the longitudinal vibration mode, m is the specimen mass (g), L is the specimen length (mm), b is the specimen width (mm), t is the specimen thickness (mm), fₗ is the fundamental longitudinal vibration frequency (Hz), and K is the correction factor.

Let T₁ = 1/f be the fundamental vibration period obtained from the fundamental frequency measured in the acoustic tests. Then, the dynamic modulus of elasticity (𝐸_dyn) was determined according to Eqs. (2) and (3).

Fig. 1. Methodology used for the excitation wave propagation test to obtain the (A) longitudinal and (B) flexural modes

Ultrasonic Waves

Ultrasonic wave propagation tests were performed using a V-Meter MK IV (James Instruments) equipped with a 500 kHz longitudinal transducer. The same specimens used in the other tests were employed, with dimensions of 20 × 20 × 300 mm. The equipment detects the acoustic pulse emitted by transducers positioned on each transverse face of the specimen. These transducers transmit electrical pulses to a piezoelectric crystal, which converts them into ultrasonic waves.

The specimen length was determined based on the wavelength (λ = V_wave / frequency), as prescribed by ABNT NBR 58000 (2007), which establishes a minimum length of 3λ. This recommendation was followed in the present study. Using Eq. 4, the modulus of elasticity was calculated based on apparent density and ultrasonic wave velocity, both adjusted to 12% moisture content.

where MOEd is the dynamic modulus of elasticity, or elastic constant (MPa), g is the acceleration due to gravity, assumed to be 10 m·s⁻², ρ is the apparent density at 12% moisture content (kg·m⁻³), and V is the wave propagation velocity (m·s⁻¹).

Stress Wave

Stress wave propagation tests were performed using a Microsecond Timer device FAKOPP (Fig. 2). This test consists of activating a transducer through an impact applied with a small hammer, thereby generating a mechanical wave. The wave is received by a sensor located at the opposite end of the specimen, allowing the travel time to be recorded. Knowing the distance traveled by the wave, the propagation velocity was determined. Measurements were performed using direct transmission. For the calculation of the dynamic modulus of elasticity (Ed), Eq. 4 was also applied.

Fig. 2. Schematic representation of the stress wave propagation test setup, showing the Fakopp Microsecond Timer, transducers, impact hammer, and the wave propagation distance measured along the wood specimen

Static Bending

To evaluate the accuracy of the proposed methods, the specimens were subjected to static bending tests to determine the modulus of elasticity. The tests followed the procedures established by ISO 13061-3 (2014) and were performed using an EMIC DL 10000 universal testing machine.

Statistical Analysis

The data were subjected to the Shapiro–Wilk test to assess normality and the Bartlett test to evaluate homoscedasticity. When significant differences were identified for any dependent variable. Student’s t-test was applied for mean comparisons. All tests were conducted at a 95% confidence level. Measures of dispersion expressed as standard error were provided to better characterize the confidence interval obtained for each analyzed variable. MiniTab 19 software was used for all statistical procedures. Subsequently, Pearson’s correlation analysis (r) was performed to evaluate the adequacy of the linear regression model.

RESULTS AND DISCUSSION

No statistically significant differences were observed between the MSN (knot-free wood) and MCN (wood with knots) groups for the evaluated properties (Table 1). This result contradicts the initial hypothesis, as knots were expected to interfere with wave propagation by disrupting fiber continuity, which may cause signal dispersion, attenuation, and reduced propagation velocity in anisotropic materials such as wood (Palma et al. 2018).

The occurrence of knots is common in young plantation-grown teak wood, particularly under short-rotation management. High stand densities adopted to promote initial height growth influence branch development and the proportion of live and dead branches. However, self-pruning may be insufficient, requiring timely silvicultural pruning to produce clear, knot-free stems (Víquez and Pérez 2005; Vongkhamho et al. 2020; Lima et al. 2021).

Table 1. Values of the Variables Determined from Dynamic Tests, with Natural Frequency Obtained from Impulse Excitation Tests and Wave Propagation Velocity Obtained from Ultrasonic and Stress Wave Tests

The mean apparent density values (~632–637 kg/m³) are consistent with those reported for juvenile teak from commercial plantations. Studies conducted by Silva-Albuês et al. (2024) and Ruan et al. (2024) indicate that the density of young Tectona grandis typically ranges between 550 and 700 kg/m³ depending on site conditions and silvicultural management. Thus, the material analyzed in this study presents physical characteristics representative of plantation-grown juvenile teak.

The mean static modulus of elasticity (MOE ≈ 9.2 GPa for both treatments) falls within the range reported for juvenile teak wood. Studies involving plantations aged between 8 and 15 years commonly report MOE values ranging from approximately 8 to 12 GPa, with stiffness increasing as tree age advances and the proportion of juvenile wood decreases (Silva-Albuês et al. 2024; Papandrea et al. 2022).

Comparative analyses between clear and knotty wood in fast-growing species, such as that presented by Ruan et al. (2024), show that significant reductions in stiffness are generally associated with large knots or high knot-to-cross-section ratios. In the present study, the absence of statistically significant differences between MSN and MCN suggests that the knots evaluated exhibited low structural severity and were not sufficient to compromise the global elastic behavior of MSN specimens.

The small difference observed between treatments (3.6%) indicates that the presence of knots had a limited influence on the modulus of rupture (MOR) and was insufficient to significantly alter the bending behavior of the wood. This finding suggests that the knots present in the specimens were not severe enough to compromise the maximum stress sustained by the material prior to failure, indicating that, under the conditions evaluated, MOR was predominantly governed by the intrinsic characteristics of the wood.

Table 2. Modulus of Elasticity is Determined from Dynamic and Static Tests for Wood with Knots and Knot-free Wood

The MOR values obtained in the present study were higher than those reported for teak wood from young plantations. Seta et al. (2023) reported values ranging from 77.2 to 80.2 MPa for 15-year-old clonal teak subjected to different thinning and pruning regimes, whereas Nugroho et al. (2024) found a mean value of 72.0 MPa for 20-year-old clonal material. These differences reinforce that the mechanical performance of wood is not determined solely by the presence of natural defects, but is strongly influenced by factors such as genetic material, growth conditions, silvicultural practices, wood density, sampling position, and testing procedures (Marini et al. 2021). Therefore, the absence of a significant difference between specimens with and without knots indicates that, when knots are of low severity, their effect on MOR may be less pronounced than the influence of the wood’s intrinsic characteristics and the conditions under which it developed.

Dynamic moduli were consistently higher than the static modulus, which reflects the instantaneous elastic response captured by acoustic methods and the absence of time-dependent viscoelastic effects during loading (Bucur 2023). Similar findings have been reported by Fathi et al. (2020) and Silva et al. (2023) in studies involving fast-growing species.

Linear regression analyses between MOEd and MOE showed high coefficients of determination, particularly for impulse excitation and stress wave methods (R² < 84%).

Table 3. Regression Equations for the Dynamic and Static Moduli of Elasticity and their Respective R² Values

Similar predictive performance has been reported by Papandrea et al. (2022), who demonstrated strong correlations between acoustic measurements and static MOE in standing trees and logs. Likewise, Duong and Ridley-Ellis (2021) and Ferreira et al. (2023) highlighted the reliability of stress wave methods for estimating mechanical properties in heterogeneous wood materials.

The impulse excitation method showed the highest predictive performance, with R² values exceeding 88% in the longitudinal mode. This result is consistent with Carrasco et al. (2017) and Silva et al. (2023), who emphasized the high accuracy of this technique due to its sensitivity to natural vibration frequencies and reduced susceptibility to minor internal discontinuities.

Ultrasonic testing exhibited lower predictive performance for knot-free wood (R² = 66.74%), although it showed improved stability in the knotty group (R² = 81.82%). This variation may be associated with wave scattering and attenuation phenomena in structurally heterogeneous materials such as juvenile wood (Solodov et al. 2004; Ettelaei et al. 2019; Afoutou et al. 2024).

Although the literature frequently associates knots with reductions in stiffness and strength (Zhou et al. 2016; Rocha et al. 2018), the present results indicate that knots in the analyzed juvenile Tectona grandis wood did not significantly affect dynamic parameters or static MOE.

Recent studies suggest that small or well-integrated knots may present adjacent regions with increased local density and anatomical rearrangements that contribute to partial structural compensation (Zhang et al. 2024). Such integration may reduce the negative impact typically expected on global stiffness, particularly when the knot does not significantly disrupt the predominant longitudinal fiber orientation.

Furthermore, juvenile wood is characterized by pronounced anatomical heterogeneity, including higher proportions of earlywood, thinner cell walls, and radial variability. These features tend to increase experimental dispersion, potentially masking subtle structural differences associated with knots (Llana et al. 2020; Duong et al. 2022; Carvalho et al. 2023).

CONCLUSIONS

This study investigated the performance of three wave-based non-destructive techniques for estimating the modulus of elasticity of juvenile wood of Tectona grandis and whether the presence of knots influences wave propagation. The research presented in this paper provides the technical foundation for using these technologies with Tectona grandis wood. The main conclusions are as follows:

Ultrasonic waves, stress waves, and impulse excitation methods can be replicated for estimating the modulus of elasticity of juvenile of wood of Tectona grandis.
The ultrasonic wave propagation method was the most sensitive to the occurrence of knots.
Among the evaluated methods, impulse excitation showed the best performance in estimating the modulus of elasticity and the lowest sensitivity to the presence of knots, as evidenced by the highest correlation coefficients for wood with and without knots.
The presence of knots in fast-growing juvenile wood did not significantly affect mechanical wave propagation. However, further studies are necessary to better clarify the influence of knots on wave attenuation and the dynamic response of juvenile wood.

ACKNOWLEDGMENTS

The authors thank the Federal University of Espírito Santo (UFES) for the support and infrastructure provided, especially the Laboratory of Wood Experimentation and Multiple Uses (LEMU). We also express our gratitude to our colleagues and collaborators.

REFERENCES CITED

ABNT NBR 58000 (2007). “Ensaios não destrutivos – Ultrassom: classificação mecânica de madeira serrada de dicotiledôneas (Projeto NBR 58 000 10001),” Associação Brasileira de Normas Técnicas (ABNT), Rio de Janeiro, Brazil.

Afoutou, J. S., Zhang, X., and Dubois, F. (2024). “Full elastic properties characterization of wood by ultrasound using a single sample,” Wood Science and Technology 58, 403-422. https://doi.org/10.1007/s00226-023-01525-y

Alves, R. C., Mantilla, J. N. R., Bremer, C. F., and Carrasco, E. V. M. (2015). “Application of acoustic tomography and ultrasonic waves to estimate stiffness constants of muiracatiara Brazilian wood,” BioResources 10(1), 1845-1856. https://doi.org/10.15376/biores.10.1.1845-1856

ASTM D2395-17 (2017). “Standard test methods for density and specific gravity (relative density) of wood and wood-based materials,” ASTM International, West Conshohocken, PA, USA.

ASTM D4442-20 (2020). “Standard test methods for direct moisture content measurement of wood and wood-based materials,” ASTM International, West Conshohocken, PA, USA.

ASTM E1876-21 (2021). “Standard test method for dynamic Young’s modulus, shear modulus, and Poisson’s ratio by impulse excitation of vibration,” ASTM International, West Conshohocken, PA, USA.

Barbosa, M. C. C., Street, J., Owens, F., and Shmulsky, R. (2019). “The effect of multiple knots in close proximity on southern pine lumber properties,” Forest Products Journal 69(4), 278-282. https://doi.org/10.13073/FPJ-D-19-00027

Bresolin, J. M., and Pravia, Z. M. C. (2021). “Caracterização experimental e analítica de vigas de madeira lamelada colada em pinus quanto à rigidez à flexão,” in: Vangelista, W. V. (Org.), Madeiras nativas e plantadas do Brasil (pp. 301-318). Editora Científica Digital. https://doi.org/10.37885/210504803

Bucur, V. (1983). “An ultrasonic method for measuring the elastic constants of wood increment cores bored from living trees,” Ultrasonics 21(3), 116-126. https://doi.org/10.1016/0041-624X(83)90031-8

Bucur, V. (2023). “A review on acoustics of wood as a tool for quality assessment,” Forests 14(8), article 1545. https://doi.org/10.3390/f14081545

Burgers, A., Montero, C., Gené Sera, J., Vilches Casals, M., Correal Modol, E., Langbour, P., and Thibaut, B. (2019). “Timber grading of Pinus uncinata, a lesser known pine species from the Pyrenean mountain range,” Maderas. Ciencia y Tecnología 21(2), 261-276. http://dx.doi.org/10.4067/S0718-221X2019005000213

Carrasco, E. V. M., Cargas, C. B., Souza, M. F., and Mantilla, J. N. R. (2017). “Avaliação das características mecânicas da madeira por meio de excitação por impulso,” Revista Matéria 2017, article e-11936. https://doi.org/10.1590/S1517-707620170005.0272

Carvalho, R. A., Silva, G. F., Pinheiro, C. J. G., Mastela, L. C., Souza, C. G. F., Gamas, A. L., and Segundinho, P. G. A. (2023). “Predição do módulo de elasticidade estático de Eucalyptus sp. por meio de duas técnicas não destrutivas,” Ciência Florestal 33(3), article e70019, 1-15. https://doi.org/10.5902/1980509870019

Duong, D. V., and Ridley-Ellis, D. (2021). “Estimating mechanical properties of clear wood from ten-year-old Melia azedarach trees using the stress wave method,” European Journal of Wood and Wood Products 79(4), 941-949. https://doi.org/10.1007/s00107-021-01664-8

Duong, D. V., Schimleck, L., and Dong, T. L. (2022). “Radial and among-clonal variations of the stress-wave velocity, wood density, and mechanical properties in 5-year-old Acacia auriculiformis clones,” BioResources 17(2), 2084- 2096. https://doi.org/10.15376/biores.17.2.2084-2096

Ettelaei, A., Layeghi, M., Zarea Hosseinabadi, H., and Ebrahimi, G. (2019). “Prediction of modulus of elasticity of poplar wood using ultrasonic technique by applying empirical correction factors,” Measurement 135, 392-399. https://doi.org/10.1016/j.measurement.2018.11.076

Fathi, H., Kazemirad, S., and Nasir, V. (2021). “Lamb wave propagation method for nondestructive characterization of the elastic properties of wood,” Applied Acoustics 171, article 107565. https://doi.org/10.1016/j.apacoust.2020.107565

Fathi, H., Nasir, V., and Kazemirad, S. (2020). “Prediction of the mechanical properties of wood using guided wave propagation and machine learning,” Construction and Building Materials 262, article 120848. https://doi.org/10.1016/j.conbuildmat.2020.120848

Ferreira, P. da S., Ramalho, F. M. G., Couto, A. M., Protásio, T. P., Monteiro, T. C., and Hein, P. R. G. (2023). “Relationship among the stiffness, wave propagation speed, density and moisture content of Pinus elliottii and Bertholletia excelsa wood specimens,” Wood Material Science and Engineering, 18(1), 151-160. https://doi.org/10.1080/17480272.2021.1996456

Hu, W., and Yu, R. (2023). “Mechanical and acoustic characteristics of four wood species subjected to bending load,” Maderas. Ciencia y Tecnología 25, e2023000100439. https://doi.org/10.4067/S0718-221X2023000100439

ISO 13061-3 (2014). “Physical and mechanical properties of wood — Test methods for small clear wood specimens — Part 3: Determination of ultimate strength in static bending,” International Organization for Standardization, Geneva, Switzerland.

Lima, I. L. de, Ranzini, M., Longui, E. L., and Barbosa, J. d. A. (2021). “Wood characterization of Tectona grandis L. F. cultivated in Brazil: A review of the last 30 years,” Research, Society and Development 10(14), article e162101421549. https://doi.org/10.33448/rsd-v10i14.21549

Llana, D. F., Short, I., and Harte, A. M. (2020). “Use of non-destructive test methods on Irish hardwood standing trees and small-diameter round timber for prediction of mechanical properties,” Annals of Forest Science 77, 1-13.

Lyu, J., Qu, H., and Chen, M. (2023). “Influence of wood knots of chinese weeping cypress on selected physical properties,” Forests 14(6), article 1148. https://doi.org/10.3390/f14061148

Marini, F., Manetti, M. C., Corona, P., Portoghesi, L., Vinciguerra, V., Tamantini, S., Kuzminsky, E., Zikeli, F., and Romagnoli, M. (2021). “Influence of forest stand characteristics on physical, mechanical properties and chemistry of chestnut wood,” Scientific Reports 11, article 1549. https://doi.org/10.1038/s41598-020-80558-w

Nugroho, W. D., Na’iem, M., Lukmandaru, G., Widiyatno, Feriawan, Y., Prastiwi, F. W., Wibowo, A., and Puspitasari, D. (2024). “Physical and mechanical properties of 20-year-old clonal teak trees in Ngawi, East Java, Indonesia,” Journal of the Korean Wood Science and Technology 52(5), 459-472. https://doi.org/10.5658/WOOD.2024.52.5.459

Palma, S. S. A., Gonçalves, R., Trinca, A. J., Costa, C. P., Reis, M. N., and Martins, G. A. (2018). “Interference from knots, wave propagation direction, and effect of juvenile and reaction wood on velocities in ultrasound tomography,” BioResources 13(2), 2834-2845. https://doi.org/10.15376/biores.13.2.2834-2845

Papandrea, S. F., Cataldo, M. F., Bernardi, B., Zimbalatti, G., and Proto, A. R. (2022). “The predictive accuracy of modulus of elasticity (MOE) in the wood of standing trees and logs,” Forests 13, article 1273. https://doi.org/10.3390/f13081273

Rocha, M. F. V., Costa, L. R., Costa, L. J., Araújo, A. C. C., Soares, B. C. D., and Hein, P. R. G. (2018). “Wood knots influence the modulus of elasticity and resistance to compression,” Floresta e Ambiente 25(4), article e20170906. https://doi.org/10.1590/2179-8087.090617

Ross, R. J. (2015). Nondestructive Evaluation of Wood: Second Edition, USDA Forest Service, Forest Products Laboratory, General Technical Report, FPL-GTR-238, 2015; 176 p. https://doi.org/10.2737/FPL-GTR-238

Ruan, Y., He, Z., Fan, S., Chen, Z., Li, M., Ma, X., and Sun, S. (2024). “Variations in physical and mechanical properties between clear and knotty wood of Chinese fir,” Forests 15(11), article 2007. https://doi.org/10.3390/f15112007

Seta, G. W., Hidayati, F., Widiyatno, and Na’iem, M. (2023). “Wood physical and mechanical properties of clonal teak (Tectona grandis) stands under different thinning and pruning intensity levels planted in Java, Indonesia,” Journal of the Korean Wood Science and Technology 51(2), 109-132. https://doi.org/10.5658/WOOD.2023.51.2.109

Silva, L. S. Z. R. S., Panzera, T. H., Garcia, C. T., Silva, D. A. L., Lahr, F. A. R., and Christoforo, A. L. (2023). “Physical-mechanical properties of Cajueiro wood estimated by non-destructive methods based on apparent density and natural vibration frequency,” Ciência Florestal 33(4), article e66096, 1-22. https://doi.org/10.5902/1980509866096

Silva-Albuês, T. A., Corradi-Pereira, B. L., Guimarães-Carvalho, A., Môra, R., and Costa-Oliveira, A. (2024). “Quality of Tectona grandis for sawn wood production,” Maderas: Ciencia y Tecnología 26, article e06. https://doi.org/10.22320/s0718221x/2024.06

Solodov, I., Pfleiderer, K., and Busse, G. (2004). “Nondestructive characterization of wood by monitoring of local elastic anisotropy and dynamic nonlinearity,” Holzforschung 58(5), 504-510. https://doi.org/10.1515/HF.2004.077

Víquez, E., and Pérez, D. (2005). “Effect of pruning on tree growth, yield, and wood properties of Tectona grandis plantations in Costa Rica,” Silva Fennica 39(3), 381-390.

Virgen-Cobos, G. H., Olvera-Licona, G., Hermoso, E., and Esteban, M. (2022). “Nondestructive techniques for determination of wood mechanical properties of urban trees in Madrid,” Forests 13, article 1381. https://doi.org/10.3390/f13091381

Vongkhamho, S., Imaya, A., Takenaka, C., Yamamoto, K., and Yamamoto, H. (2020). “Correlations among tree quality, stand characteristics, and site characteristics in plantation teak in mountainous areas of Lao PDR,” Forests 11(7), 777.

Zhang, X., Dubois, F., Sauvat, N., and Takarli, M. (2022). “Hybrid numerical method for the ultrasonic wave propagation velocity in orthotropic materials,” Wood Science and Technology 56(6), 1605-1630. https://doi.org/10.1007/s00226-022-01416-8

Zhang, X., Sun, H., Xu, G., Duan, Y., Van den Bulcke, J., Van Acker, J., and Shi, J. (2024). “Understanding the effect of knots on mechanical properties of Chinese fir under bending test by using X-ray computed tomography and digital image correlation,” Forests 15(1), article 174. https://doi.org/10.3390/f15010174

Zhou, Z., Yin, J., Zhou, S., Zhou, H., and Zhang, Y. (2016). “Detection of knot defects on coniferous wood surface using near infrared spectroscopy and chemometrics,” BioResources 11(4), 9533-9546. https://doi.org/10.15376/biores.11.4.9533-9546

Article submitted: December 15, 2025; Peer review completed: January 31, 2026; Revised version received: July 31, 2026; Accepted: August 2, 2026; Published: August 19, 2026.

DOI: 10.15376/biores.21.4. 9932-9946

APPENDIX

(A)

(B)

Fig. S1 (A and B). Linear regression between the dynamic modulus of elasticity (MOEd) obtained by impulse excitation in the flexural and longitudinal modes and the static modulus of elasticity (MOE) for juvenile Tectona grandis wood with knots (MCN)

Fig. S2 (A and B). Linear regression between the dynamic modulus of elasticity (MOEd) obtained by impulse excitation in the flexural and longitudinal modes and the static modulus of elasticity (MOE) for juvenile Tectona grandis wood without knots (MSN)

Fig. S3. Linear regression between the dynamic modulus of elasticity (MOEd) obtained by the stress wave method and the static modulus of elasticity (MOE) for juvenile Tectona grandis wood with knots (MCN)

Fig. S4. Linear regression between the dynamic modulus of elasticity (MOEd) obtained by the stress wave method and the static modulus of elasticity (MOE) for juvenile Tectona grandis wood without knots (MSN)