An artificial neural network model for predicting compression strength of heat treated woods and comparison with a multiple linear regression model

TİRYAKİ S., AYDIN A.

CONSTRUCTION AND BUILDING MATERIALS, cilt.62, ss.102-108, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 62
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1016/j.conbuildmat.2014.03.041
  • Dergi Adı: CONSTRUCTION AND BUILDING MATERIALS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.102-108
  • Anahtar Kelimeler: Artificial neural network, Heat treatment, Compression strength, Multiple linear regression, PINE PINUS-SYLVESTRIS, TECHNOLOGICAL PROPERTIES, MECHANICAL-PROPERTIES, SURFACE-ROUGHNESS, BONDING STRENGTH, HIGH-TEMPERATURE, PARTICLEBOARD, BEHAVIOR, MOE
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

This paper aims to design an artificial neural network model to predict compression strength parallel to grain of heat treated woods, without doing comprehensive experiments. In this study, the artificial neural network results were also compared with multiple linear regression results. The results indicated that artificial neural network model provided better prediction results compared to the multiple linear regression model. Thanks to the results of this study, strength properties of heat treated woods can be determined in a short period of time with low error rates so that usability of such wood species for structural purposes can be better understood. (C) 2014 Elsevier Ltd. All rights reserved.