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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
Original
  • 53 Downloads

Abstract

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.

Notes

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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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