Skip to main content
SpringerLink
Log in

Longitudinal, transverse and ultrasound vibration for the prediction of stiffness using models incorporating features in Pinus sylvestris timber

  • Original Article
  • Published:
European Journal of Wood and Wood Products Aims and scope Submit manuscript

Abstract

Non-destructive testing was used to predict the static modulus of elasticity (MOES) of Scots pine (Pinus sylvestris) timber from the northeast of Spain. Three vibration tests were performed, longitudinal, flatwise and edgewise, to obtain the dynamic modulus of elasticity (MOEdyn) based on the fundamental resonant frequencies. The MOEdyn was additionally obtained from ultrasound tests. Measurements of different features were performed of the various samples, which were also subjected to a bending test to find the MOES. Different types of models, simple linear regression (SLR), multiple linear regression (MLR) and artificial neural network (ANN), were generated to predict the MOES based on the study variables. The predictive capacity of the different models was analysed by comparing the root mean square error (RMSE) obtained using the tenfold cross-validation method. The vibration techniques showed a better MOES prediction than the ultrasound techniques. The MOEdyn obtained from the fundamental resonant frequency of the edgewise flexural vibration (MOEEV) was the variable that best predicted the MOES. The error of the SLR with MOEEV was not significantly improved by any other model, whether univariate or multivariate. The ANN-based models did not significantly improve the error of the MLR-based models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Alexander DLJ, Tropsha A, Winkler DA (2015) Beware of R2: simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models. J Chem Inf Model 55:1316–1322

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Arriaga F, Iniguez-Gonzalez G, Esteban M, Divos F (2012) Vibration method for grading of large cross-section coniferous timber species. Holzforschung 66:381–387

    Article  CAS  Google Scholar 

  • Arriaga F, Monton J, Segues E, Iniguez-Gonzalez G (2014) Determination of the mechanical properties of radiata pine timber by means of longitudinal and transverse vibration methods. Holzforschung 68:299–305

    Article  CAS  Google Scholar 

  • ASTM Standard (2015) E1876–15 standard test method for dynamic young’s modulus, shear modulus, and Poisson’s ratio by impulse excitation of vibration. ASTM, West Conshohocken

    Google Scholar 

  • Audacity Team (2015) Audacity®. Version 2.1.12. Audio editor and recorder. http://audacityteam.org/

  • Baar J, Tippner J, Rademacher P (2015) Prediction of mechanical properties—modulus of rupture and modulus of elasticity—of five tropical species by nondestructive methods. Maderas-Cienc Tecnol 17:239–252

    Google Scholar 

  • Barrett JD, Hong JP (2010) Moisture content adjustments for dynamic modulus of elasticity of wood members. Wood Sci Technol 44:485–495

    Article  CAS  Google Scholar 

  • Brancheriau L, Bailleres H (2002) Natural vibration analysis of clear wooden beams: a theoretical review. Wood Sci Technol 36:347–365

    Article  CAS  Google Scholar 

  • Chauhan S, Sethy A (2016) Differences in dynamic modulus of elasticity determined by three vibration methods and their relationship with static modulus of elasticity. Maderas-Cienc Tecnol 18:373–382

    CAS  Google Scholar 

  • Cheng F, Hu Y (2011) Reliability analysis of timber structure design of poplar lumber with nondestructive testing methods. BioResources 6:3188–3198

    CAS  Google Scholar 

  • Cho CL (2007) Comparison of three methods for determining Young’s modulus of wood. Taiwan J Forest Sci 22:297–306

    Google Scholar 

  • Esteban LG, Fernández FG, De Palacios P (2009) MOE prediction in Abies pinsapo Boiss. timber: application of an artificial neural network using non-destructive testing. Comput Struct 87:1360–1365

    Article  Google Scholar 

  • European Standard (2002a) EN 13183-1. Moisture content of a piece of sawn timber. Part 1: determination by oven dry method. European Comittee for Standardization (CEN), Brussels

    Google Scholar 

  • European Standard (2002b) EN 13183-2 Moisture content of a piece of sawn timber—part 2: estimation by electrical resistance method. European Comittee for Standardization (CEN), Brussels

    Google Scholar 

  • European Standard (2012) EN 408:2010+A1:2012. Timber structures. Structural timber and glued laminated timber. Determination of some physical and mechanical properties. European Comittee for Standardization (CEN), Belgium

    Google Scholar 

  • European Standard (2016) EN 338:2016. Structural timber Strength classes. European Comittee for Standardization (CEN), Brussels

    Google Scholar 

  • European Standard (2018) EN 1309-3:2018. Round and sawn timber—methods of measurements—part 3: features and biological degradations. European Comittee for Standardization (CEN), Brussels

    Google Scholar 

  • Faydi Y, Brancheriau L, Pot G, Collet R (2017) Prediction of oak wood mechanical properties based on the statistical exploitation of vibrational response. BioResources 12:5913–5927

    Article  CAS  Google Scholar 

  • García-Iruela A, Fernández FG, Esteban LG, De Palacios P, Simón C, Arriaga F (2016) Comparison of modelling using regression techniques and an artificial neural network for obtaining the static modulus of elasticity of Pinus radiata D. Don. timber by ultrasound. Compos Part B-Eng 96:112–118

    Article  Google Scholar 

  • Guntekin E, Emiroglu Z, Yilmaz T (2013) Prediction of Bending properties for Turkish Red Pine (Pinus brutia Ten.) Lumber using stress wave method. BioResources 8:231–237

    CAS  Google Scholar 

  • Halabe UB, Bidigalu GM, GangaRao HVS, Ross RJ (1997) Nondestructive evaluation of green wood using stress wave and transverse vibration techniques. Mater Eval 55:1013–1018

    Google Scholar 

  • Hashim UR, Hashim SZM, Muda AK (2016) Performance evaluation of multivariate texture descriptor for classification of timber defect. Optik 127:6071–6080

    Article  Google Scholar 

  • Hassan KTS, Horacek P, Tippnera J (2013) Evaluation of stiffness and strength of scots pine wood using resonance frequency and ultrasonic techniques. BioResources 8:1634–1645

    Article  CAS  Google Scholar 

  • Hodousek M, Dias M, Martins C, Marques A, Böhm M (2016) Comparison of non-destructive methods based on natural frequency for determining the modulus of elasticity of Cupressus lusitanica and Populus x canadiensis. BioResources 12:270–282

    Article  CAS  Google Scholar 

  • How SS, Williamson CJ, Carradine D, Tan YE, Cambridge J, Pang S (2014) Predicting the young’s modulus of defect free radiata pine shooks in finger-jointing using resonance frequency. Maderas-Cienc Tecnol 16:435–444

    Google Scholar 

  • Ilic J (2001) Relationship among the dynamic and static elastic properties of air-dry Eucalyptus delegatensis R. Baker Holz Roh Werkst 59:169–175

    Article  Google Scholar 

  • Íñiguez González G, Arriaga Martitegui F, Esteban Herrero M, Argüelles Alvárez R (2007) Los métodos de vibración como herramienta no destructiva para la estimación de las propiedades resistentes de la madera aserrada estructural [Vibration methods as non-destructive tool for structural properties assessment of sawn timber]. Inf Constr 59:97–105

    Article  Google Scholar 

  • Larsson D, Ohlsson S, Perstorper M, Brundin J (1998) Mechanical properties of sawn timber from Norway spruce. Holz Roh Werkst 56:331–338

    Article  Google Scholar 

  • Lever J, Krzywinski M, Altman N (2016) Model selection and overfitting. Nat Methods 13:703–704

    Article  CAS  Google Scholar 

  • Liu Y, Gong M, Li L, Chui YH (2014) Width effect on the modulus of elasticity of hardwood lumber measured by non-destructive evaluation techniques. Constr Build Mater 50:276–280

    Article  Google Scholar 

  • Mania P, Siuda F, Roszyk E (2020) Effect of slope grain on mechanical properties of different wood species. Materials 13:1503

    Article  CAS  PubMed Central  Google Scholar 

  • Nocetti M, Brunetti M, Bacher M (2016) Efficiency of the machine grading of chestnut structural timber: prediction of strength classes by dry and wet measurements. Mater Struct 49:4439–4450

    Article  Google Scholar 

  • Pardos JA, Werner L, Günter W (1990) Morphological and chemical aspects of Pinus sylvestris L. from Spain. Holzforschung 44:143–146

    Article  CAS  Google Scholar 

  • Pommier R, Breysse D, Dumail JF (2013) Non-destructive grading of green Maritime pine using the vibration method. Eur J Wood Prod 71:663–673

    Article  CAS  Google Scholar 

  • R Core Team (2019) R: A language and environment for statistical computing. Version 3.6.1. R Foundation for Statistical Computing, Vienna. http://www.R-project.org/

  • Ranta-Maunus A, Denzler JK, Stapel P (2011) Strength of European timber. Part 2. Properties of spruce and pine tested in Gradewood project. VTT Technical Research Centre of Finland. VTT Working Papers, No. 179 http://www.vtt.fi/inf/pdf/workingpapers/2011/W179.pdf

  • Refaeilzadeh P, Tang L, Liu H (2009) Cross-validation. In: Liu L, Özsu MT (eds) Encyclopedia of database systems. Springer, US, Boston, pp 532–538

    Chapter  Google Scholar 

  • Sales A, Candian M, De Salles CV (2011) Evaluation of the mechanical properties of Brazilian lumber (Goupia glabra) by nondestructive techniques. Constr Build Mater 25:1450–1454

    Article  Google Scholar 

  • Sandoz JL (1989) Grading of construction timeber by ultrasound. Wood Sci Technol 23:95–108

    Article  Google Scholar 

  • Simic K, Gendvilas V, O’Reilly C, Harte AM (2019) Predicting structural timber grade-determining properties using acoustic and density measurements on young Sitka spruce trees and logs. Holzforschung 73:139–149

    Article  CAS  Google Scholar 

  • Spycher M, Schwarze FWMR, Steiger R (2008) Assessment of resonance wood quality by comparing its physical and histological properties. Wood Sci Technol 42:325–342

    Article  CAS  Google Scholar 

  • Tanaka T, Tanaka T, Nagao H, Kato H (1996) A preliminary investigation on evaluation of strength of soft wood timbers by neural network. Proceeding of the 10th International Symposium on Nondestructive Testing of Wood, Lausanne. pp. 323–329

  • Tetko IV, Livingstone DJ, Luik AI (1995) Neural Network Studies. 1. Comparison of Overfitting and Overtraining. J Chem Inf Comput Sci 35:826–833

    Article  CAS  Google Scholar 

  • Villasante A, Iniguez-Gonzalez G, Puigdomenech L (2019) Comparison of various multivariate models to estimate structural properties by means of non-destructive techniques (NDTs) in Pinus sylvestris L. timber. Holzforschung 73:331–338

    Article  CAS  Google Scholar 

  • Waikato University (2014) WEKA software. Version 3.6.12. Waikato University, Hamilton. https://www.cs.waikato.ac.nz/ml/weka/

  • Walker J (1993) Primary wood processing: principles and practice. Chapman & Hall, London, pp 348–352

    Book  Google Scholar 

  • Wang SY, Chen JH, Tsai MJ, Lin CJ, Yang TH (2008) Grading of softwood lumber using non-destructive techniques. J Mater Process Tech 208:149–158

    Article  Google Scholar 

  • Weaver W, Timoshenko S, Young DH (1990) Vibration problems in engineering. Wiley, New York

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Agricultural and Forest Engineering, University of Lleida, Av. Rovira Roure, 191, 25198, Lleida, Spain

    Álvaro Fernández-Serrano & Antonio Villasante

Authors
  1. Álvaro Fernández-Serrano
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Antonio Villasante
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Álvaro Fernández-Serrano.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fernández-Serrano, Á., Villasante, A. Longitudinal, transverse and ultrasound vibration for the prediction of stiffness using models incorporating features in Pinus sylvestris timber. Eur. J. Wood Prod. 79, 1541–1550 (2021). https://doi.org/10.1007/s00107-021-01707-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00107-021-01707-0

Search

Navigation