Applying machine learning to predict the tensile shear strength of bonded beech wood as a function of the composition of polyurethane prepolymers and various pretreatments

  • 94 Accesses


The present study showed that machine learning can be used to predict the tensile shear strength of bonded beech wood as a function of the composition of polyurethane prepolymers and various pretreatments. A comprehensive experimental data set was used for training and testing the algorithms support vector machines, random forest and artificial neural networks. Within the framework of the experiments, the structure–property relationships of 1C PUR prepolymers were analyzed by systematical variation of the structural parameters urea and urethane group content, cross-link density, ethylene oxide content, and the functionality via isocyanate (NCO) or polyether component. The bonded wood joints were tested according to DIN EN 302-1. Prior to testing, the shear test specimens were pretreated according to procedures A1 and A4, five temperature steps (5, 40, 70, 150 and 200 °C) and two alternating climates. The complete data set (N = 2840) was preprocessed and split into a training set and a test set using tenfold cross-validation. The performance of the algorithms was evaluated with the coefficient of determination (R2), root-mean-square error (RMSE) and mean absolute percentage error (MAPE). All machine learning algorithms revealed a high accuracy, but the artificial neural network showed the best performance with R2= 0.92, RMSE = 0.948 and a MAPE of 9.21. The work paves the way for future machine learning applications in the field of adhesive bonding technology and may enable a fast and effective development of new adhesives and enhance the efficiency of their application.

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

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 199

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2


  1. Bardak S, Tiryaki S, Bardak T, Aydin A (2016a) Predictive performance of artificial neural network and multiple linear regression models in predicting adhesive bonding strength of wood. Strength Mater 48:811–824.

  2. Bardak S, Tiryaki S, Nemli G, Aydın A (2016b) Investigation and neural network prediction of wood bonding quality based on pressing conditions. Int J Adhes Adhes 68:115–123.

  3. Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 43:3–31

  4. Bishop CM (2006) Pattern recognition and machine learning (information science and statistics). Springer, New York

  5. Breiman L (2001) Random forests. Mach Learn 45:5–32.

  6. Burden F, Winkler D (2009) Bayesian regularization of neural networks. In: Livingstone DJ (ed) Artificial neural networks: methods and applications. Humana Press, Totowa, pp 23–42.

  7. Cao M, Alkayem NF, Pan L, Novák D (2016) Advanced methods in neural networks-based sensitivity analysis with their applications in civil engineering. In: Rosa JLG (ed) Artificial neural networks—models and applications. InTech, Rijeka, p 13.

  8. Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T (2018) The rise of deep learning in drug discovery. Drug Discov Today 23(6):1241–1250.

  9. Clauß S, Allenspach K, Gabriel J, Niemz P (2010) Improving the thermal stability of one-component polyurethane adhesives by adding filler material. Wood Sci Technol 45:383–388.

  10. Clauß S, Dijkstra DJ, Gabriel J, Kläusler O, Matner M, Meckel W, Niemz P (2011) Influence of the chemical structure of PUR prepolymers on thermal stability. Int J Adhes Adhes 31:513–523.

  11. DIN EN 386 (2001) Glued laminated timber—Performance requirements and minimum production requirements. Beuth, Berlin

  12. DIN EN 302-1 (2004) Adhesives for load-bearing timber structures—Test methods-Part 1: Determination of bond strength in longitudinal tensile shear strength. Beuth, Berlin

  13. DIN EN 1995-1-1 (2010) Eurocode 5: Design of timber structures—Part 1-1: General-Common rules and rules for buildings. Beuth, Berlin

  14. DIN EN 14080 (2011) Timber structures—Glued laminated timber and glued solid timber-Requirements. Beuth, Berlin

  15. DIN EN 15425 (2015) Adhesives-One component polyurethane (PUR) for load-bearing timber structures—Classification and performance requirements. Beuth, Berlin

  16. Foresee FD, Hagan MT (1997) Gauss-Newton approximation to Bayesian learning. In: Proceedings of international conference on neural networks (ICNN’97), vol 1933, pp 1930–1935.

  17. Hajmeer MN, Basheer IA, Najjar YM (1997) Computational neural networks for predictive microbiology II. Application to microbial growth. Int J Food Microbiol 34:51–66

  18. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313:504–507.

  19. Hinton G, Deng L, Yu D et al (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29:82–97.

  20. Kira K, Rendell LA (1992) A practical approach to feature selection. In: Paper presented at the proceedings of the ninth international workshop on machine learning, Aberdeen, Scotland, UK

  21. Kläusler O (2007) Untersuchung zur Auswirkung der Zusammensetzung von Polyurethan-Prepolymeren auf die Verklebungsgüte von Buchenholz. (Investigation of the effect of the composition of polyurethane prepolymers on the bonding quality of beech wood). Diploma Thesis, Hamburg University, Hamburg

  22. Kläusler O, Rehm K, Elstermann F, Niemz P (2014) Influence of wood machining on tensile shear strength and wood failure percentage of one-component polyurethane bonded wooden joints after wetting. Int Wood Prod J 5:18–26.

  23. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Paper presented at the proceedings of the 14th international joint conference on Artificial intelligence, vol 2, Montreal, Quebec, Canada

  24. Korley LTJ, Pate BD, Thomas EL, Hammond PT (2006) Effect of the degree of soft and hard segment ordering on the morphology and mechanical behavior of semicrystalline segmented polyurethanes. Polymer 47:3073–3082.

  25. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444.

  26. Mansfield SD, Iliadis L, Avramidis S (2007) Neural network prediction of bending strength and stiffness in western hemlock (Tsuga heterophylla Raf.). Holzforschung 61:707.

  27. Noble PA, Almeida JS, Lovell CR (2000) Application of neural computing methods for interpreting phospholipid fatty acid profiles of natural microbial communities. Appl Environ Microbiol 66:694–699

  28. Richter K, Schirle M (2002) Behaviour of 1 K PUR adhesives under increased moisture and temperature conditions. In: Teischinger, Stingl (eds) Lignovisionen, Proceedings of the international Symposium on Wood Based Materials. BOKU, Vienna, pp 153–158

  29. Richter K, Steiger R (2005) Thermal stability of wood-wood and wood-FRP bonding with polyurethane and epoxy adhesives. Adv Eng Mater 7:419–426.

  30. Richter K, Pizzi A, Despres A (2006) Thermal stability of structural one-component polyurethane adhesives for wood—structure-property relationship. J Appl Polym Sci 102:5698–5707.

  31. Schrödter A, Niemz P (2006) Investigation on the failure behaviour of glue joints at high temperatures and relative humidity. Holztechnologie 47:24–32

  32. Šebenik U, Krajnc M (2007) Influence of the soft segment length and content on the synthesis and properties of isocyanate-terminated urethane prepolymers. Int J Adhes Adhes 27:527–535.

  33. Snoek J, Larochelle H, Adams RP (2012) Practical bayesian optimization of machine learning algorithms. arXiv e-prints

  34. Tiryaki S, Bardak S, Bardak T (2015) Experimental investigation and prediction of bonding strength of Oriental beech (Fagus orientalis Lipsky) bonded with polyvinyl acetate adhesive. J Adhes Sci Technol 29:2521–2536.

  35. Vapnik VN (1995) The nature of statistical learning theory. Springer, New York

Download references


The authors would like to express their gratitude to the Bayer Material Science AG, Leverkusen, for providing the 1C PUR prepolymers. Furthermore, the authors would like to thank Prof. Dr. P. Niemz for his professional support and Martin Arnold as well as Dr. Mirko Luković for proofreading the manuscript.

Author information

Correspondence to Mark Schubert or Oliver Kläusler.

Ethics declarations

Conflict of interest

The authors declare that they have 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

Verify currency and authenticity via CrossMark

Cite this article

Schubert, M., Kläusler, O. Applying machine learning to predict the tensile shear strength of bonded beech wood as a function of the composition of polyurethane prepolymers and various pretreatments. Wood Sci Technol 54, 19–29 (2020) doi:10.1007/s00226-019-01144-6

Download citation