European Journal of Wood and Wood Products

, Volume 77, Issue 4, pp 645–659 | Cite as

Performance evaluation of multiple adaptive regression splines, teaching–learning based optimization and conventional regression techniques in predicting mechanical properties of impregnated wood

  • Sebahattin Tiryaki
  • Hüseyin Tan
  • Selahattin BardakEmail author
  • Murat Kankal
  • Sinan Nacar
  • Hüseyin Peker


Understanding the mechanical behaviour of impregnated wood is crucial in making a preliminary decision on the usability of such woods for structural purposes. In this paper, by considering concentration (1, 3 and 5%), pressure (1, 1.5 and 2 atm.), and time (30, 60, 90 and 120 min), an experimental study was performed, and the mechanical behaviour of impregnated wood was determined as a result of the experimental process. Multiple adaptive regression splines (MARS), teaching–learning based optimization (TLBO) algorithms and conventional regression analysis (CRA) were applied to different regression functions by using experimentally obtained data. The functions were checked against each other to detect the best equation for each parameter and to assess performances of MARS, TLBO and CRA methods in the prediction of mechanical properties. The experimental results showed that higher values of mechanical properties were obtained when lower concentration, pressure and time were chosen. Overall, all the functions successfully predicted the mechanical properties. However, the MARS and TLBO provided better accuracy in predicting the mechanical properties. The modeling results indicated that the MARS and TLBO are promising new methods in predicting the mechanical properties of impregnated wood. With the use of these methods, the mechanical behavior of impregnated wood could be determined with high levels of accuracy. Thus, the proposed methods may facilitate a preliminary decision concerning the usability of such woods for areas where the mechanical properties are important. Finally, the employment of MARS and TLBO algorithms by practitioners in the wood industry is encouraged and recommended for future studies.



  1. Amiri B (2012) Application of teaching–learning-based optimization algorithm on cluster analysis. J Basic Appl Sci Res 2(11):11795–11802Google Scholar
  2. ASTM D 1413 (1976) Standard test method of testing wood preservatives by laboratory soil block cultures, 1976: annual book of ASTM standards, USA, pp 452–460Google Scholar
  3. Bas D, Boyacı IH (2007) Modeling and optimization I: usability of response surface methodology. J Food Eng 78:836–845Google Scholar
  4. Bayram A, Uzlu E, Kankal M, Dede T (2015) Modeling stream dissolved oxygen concentration using teaching–learning based optimization algorithm. Environ Earth Sci 73:6565–6576Google Scholar
  5. Baysal E, Yalinkilic MK (2005) A new boron impregnation technique of wood by vapor boron of boric acid to reduce leaching boron from wood. Wood Sci Technol 39:187–198Google Scholar
  6. Behzad HM, Ashori A, Tarmian A, Tajvidi M (2012) Impacts of wood preservative treatments on some physico-mechanical properties of wood flour/high density polyethylene composites. Const Build Mater 35:246–250Google Scholar
  7. Chen T, Hong Z, F-a Deng, Yang X, Wei J, Cui M (2015) A novel selective ensemble classification of microarray data based on teaching-learning-based optimization. Int J Multimed Ubiquitous Eng 10(6):203–218Google Scholar
  8. Cheng MY, Cao MT (2014) Evolutionary multivariate adaptive regression splines for estimating shear strength in reinforced-concrete deep beams. Eng Appl Artif Intell 28:86–96Google Scholar
  9. Das SK, Suman S (2015) Prediction of lateral load capacity of pile in clay using multivariate adaptive regression spline and functional network. Arab J Sci Eng 40(6):1565–1578Google Scholar
  10. Dede T (2013) Optimum design of grillage structures to LRFD-AISC with teaching–learning based optimization. Struct Multidisc Optim 48:955–964Google Scholar
  11. Değirmentepe S, Baysal E, Türkoğlu T, Toker H, Deveci E (2015) Some properties of Turkish sweetgum balsam (Styrax Liquidus) impregnated oriental beech wood part II: decay resistance, mechanical, and thermal properties. Wood Res 60(4):591–604Google Scholar
  12. Deo RC, Kisi O, Singh VP (2017) Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model. Atmos Res 184:149–175Google Scholar
  13. Dey P, Das AK (2016) Application of multivariate adaptive regression spline-assisted objective function on optimization of heat transfer rate around a Cylinder. Nucl Eng Technol 48:1315–1320Google Scholar
  14. Eslah F, Enayati AA, Tajvidi M, Faezipour MM (2012) Regression models for the prediction of poplar particleboard properties based on urea formaldehyde resin content and board density. J Agric Sci Tech 14:1321–1329Google Scholar
  15. Esteban LG, Fernández FG, de Palacios P, Conde M (2009) Artificial neural networks in variable process control: application in particleboard manufacture. Invest Agrar Sist Recur For 18(1):92–100Google Scholar
  16. Fernandez FG, Esteban LG, de Palacios P, Navarro N, Conde M (2008) Prediction of standard particleboard mechanical properties utilizing an artificial neural network and subsequent comparison with a multivariate regression model. Invest Agrar Sist R 17:178–187Google Scholar
  17. Fernandez FG, DePalacios P, Esteban LG, Iruela AG, Rodrigo BG, Menasalvas E (2012) Prediction of MOR and MOE of structural plywood board using an artificial neural network and comparison with a multivariate regression model. Compos B 43:3528–3533Google Scholar
  18. Friedman JH (1991) Multivariate adaptive regression splines (with Discussion). Ann Stat 19(1):1–141Google Scholar
  19. Hashemi SKH, Latibari AJ, Khademi-Eslam H, Alamuti RF (2010) Effect of boric acid treatment on decay resistance and mechanical properties of poplar wood. BioResources 5(2):690–698Google Scholar
  20. Kecebas A, Yabanova I, Yumurtaci M (2012) Artificial neural network modeling of geothermal district heating system thought exergy analysis. Energy Convers Manage 64:206–212Google Scholar
  21. Khuntia S, Mujtaba H, Patra C, Farooq K, Sivakugan N, Das BM (2015) Prediction of compaction parameters of coarse grained soil using multivariate adaptive regression splines (MARS). Int J Geotech Eng 9(1):79–88Google Scholar
  22. Lin WW, Yu DY, Wang S, Zhang CY, Zhang SQ, Tian HY et al (2015) Multi-objective teaching–learning-based optimization algorithm for reducing carbon emissions and operation time in turning operations. Eng Optim 47:994–1007Google Scholar
  23. Ozciftci A, Ayar S, Baysal E, Toker H (2011) The effects of some impregnation parameters on modulus of rupture and modulus of elasticity of wood. Wood Res 56(2):277–284Google Scholar
  24. Pawar PJ, Rao RV (2013) Parameter optimization of machining processes using teaching–learning-based optimization algorithm. Int J Adv Manuf Technol 67:995–1006Google Scholar
  25. Rao RV, More KC (2015) Optimal design of the heat pipe using TLBO (teaching–learning-based optimization) algorithm. Energy 80:535–544Google Scholar
  26. Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43:303–315Google Scholar
  27. Rao RV, Savsani VJ, Vakharia DP (2012) Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Inform Sci 183:1–15Google Scholar
  28. Roy PK (2013) Teaching learning based optimization for short-term hydrothermal scheduling problem considering valve point effect and prohibited discharge constraint. Int J Elect Power Energy Syst 53:10–19Google Scholar
  29. Samui P (2013) Multivariate adaptive regression spline (MARS) for prediction of elastic modulus of jointed rock mass. Geotech Geol Eng 31:249–253Google Scholar
  30. Shanu SA, Das AK, Rahman MM, Ashaduzzaman M (2015) Effect of chromate–copper–boron preservative treatment on physical and mechanical properties of Raj koroi (Albizia richardiana) wood. Bangladesh J Sci Ind Res 50(3):189–192Google Scholar
  31. Sharda VN, Patel RM, Prasher SO, Ojasvi PR, Prakash C (2006) Modeling runoff from middle Himalayan watersheds employing artificial intelligence techniques. Agric Water Manage 83:233–242Google Scholar
  32. Simsek H, Baysal E (2015) Some physical and mechanical properties of borate-treated oriental beech wood. Drvna Ind 66(2):97–103Google Scholar
  33. Suman S, Khan SZ, Das SK, Chand SK (2016) Slope stability analysis using artificial intelligence techniques. Nat Hazards 84(20):727–748Google Scholar
  34. Tan H, Ulusoy H, Peker H (2017) The effects of impregnation with barite (BaSO4) on the physical and mechanical properties of wood materials. J Bartin Faculty For 19(2):160–165Google Scholar
  35. Tiryaki S, Hamzacebi C (2014) Predicting modulus of rupture (MOR) and modulus of elasticity (MOE) of heat treated woods by artificial neural networks. Measurement 49:266–274Google Scholar
  36. 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(23):2521–2536Google Scholar
  37. Togan V (2013) Design of pin jointed structures using teaching-learning based optimization. Struct Eng Mech 47(2):209–225Google Scholar
  38. Toker H, Baysal E, Simsek H, Senel A, Sonmez A, Altinok M, Ozcifci A, Yapıcı F (2009) Effects of some environmentally-friendly fire-retardant boron compounds on modulus of rupture and modulus of elastıcıty of wood. Wood Res 54(1):77–88Google Scholar
  39. Tomak ED, Viitanen H, Yildiz UC, Hughes M (2011) The combined effects of boron and oil heat treatment on the properties of beech and Scots pine wood. Part 2: water absorption, compression strength, color changes, and decay resistance. J Mater Sci 46(3):608–615Google Scholar
  40. TS 2474 (1976) Wood-determination of ultimate strength in static bending. Institute of Turkish Standards, AnkaraGoogle Scholar
  41. TS 2595 (1977) Wood-testing in compression parallel to grain. Institute of Turkish Standards, AnkaraGoogle Scholar
  42. Uluer O, Kırmacı V, Atas S (2009) Using the artificial neural network model for modeling the performance of the counter flow vortex tube. Expert Syst Appl 36:12256–12263Google Scholar
  43. Uzlu E, Kömürcü Mİ, Kankal M, Dede T, Öztürk HT (2014) Prediction of berm geometry using a set of laboratory tests combined with teaching–learning-based optimization and artificial bee colony algorithms. Appl Ocean Res 48:103–113Google Scholar
  44. Villasante A, Laina R, Rojas JAM, Rojas IM, Vignote S (2013) Mechanical properties of wood from Pinus sylvestris L. treated with light organic solvent preservative and with waterbone copper azole. For Syst 22(3):416–422Google Scholar
  45. Winandy JE (1995) The effects of waterborne preservative treatment on mechanical properties: a review. Proc Am Wood Preservers’ Assoc Woodstock MD 91:17–33Google Scholar
  46. Xia K, Gao L, Li W, Chao KM (2014) Disassembly sequence planning using a simplified teaching–learning-based optimization algorithm. Adv Eng Inf 28:518–527Google Scholar
  47. Yang H, Cheng W, Han G (2015) Wood modification at high temperature and pressurized steam: a relational model of mechanical properties based on a neural network. BioResources 10(3):5758–5776Google Scholar
  48. Yapıcı F, Ulucan D (2012) Prediction of modulus of rupture and modulus of elasticity of heat treated Anatolian chestnut (Castanea sativa) wood by fuzzy logic classifier. Drvna Ind 63:37–43Google Scholar
  49. Yildiz UC, Temiz A, Gezer ED, Yildiz S (2004) Effects of the wood preservatives on mechanical properties of yellow pine (Pinus sylvestris L.) wood. Build Environ 39:1071–1075Google Scholar
  50. Zhang W, Goh ATC (2016) Multivariate adaptive regression splines and neural network models for prediction of pile drivability. Geosci Front 7:45–52Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Sebahattin Tiryaki
    • 1
  • Hüseyin Tan
    • 2
  • Selahattin Bardak
    • 3
    Email author
  • Murat Kankal
    • 4
  • Sinan Nacar
    • 5
  • Hüseyin Peker
    • 6
  1. 1.Arsin Vocational SchoolKaradeniz Technical UniversityTrabzonTurkey
  2. 2.Vocational School of Technical SciencesRecep Tayyip Erdoğan UniversityRizeTurkey
  3. 3.Department of Industrial EngineeringSinop University, Faculty of Engineering and ArchitectureSinopTurkey
  4. 4.Department of Civil EngineeringUludağ University, Faculty of EngineeringBursaTurkey
  5. 5.Department of Civil EngineeringKaradeniz Technical University, Faculty of EngineeringTrabzonTurkey
  6. 6.Department of Forest Industry EngineeringArtvin Çoruh University, Faculty of ForestryArtvinTurkey

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