Identification of water/cement ratio of cement pastes, basing on the microstructure image analysis data and using artificial neural network

ÖZTÜRK A. U., ÖNAL O.

KSCE Journal of Civil Engineering, cilt.17, sa.4, ss.763-768, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 17 Sayı: 4
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1007/s12205-013-0156-9
  • Dergi Adı: KSCE Journal of Civil Engineering
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.763-768
  • Anahtar Kelimeler: paste, backscattered electron imaging, microstructure, compressive strength, artificial neural network
  • Manisa Celal Bayar Üniversitesi Adresli: Evet

Özet

Artificial Neural Network (ANN) analysis has been established to forecast the Water/Cement (w/c) ratio values of cement pastes by using image analysis techniques in the scope of this study. W/c ratio values have reasonably great effects on the performance of cement based structural members. The service life or ultimate performances such as strength and durability characteristics are strongly affected by w/c ratios of cementitious materials. In this study, the relationship between microstructural phases such as unhydrated cement part, hydration products, capillary porosity, and w/c ratios predicted by ANN analysis, has been established. The predicted values are compared with estimated values obtained by proposed method in the literature. The study indicated that, using a contemporary data analysis technique, which is capable of searching nonlinear relationships more thoroughly, would result in more realistic prediction of the w/c ratios compared to the proposed method. © 2013 Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg.