Modeling corrosion currents of reinforced concrete using ANN

TOPÇU İ. B., Boga A. R., HOCAOĞLU F. O.

AUTOMATION IN CONSTRUCTION, vol.18, no.2, pp.145-152, 2009 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 18 Issue: 2
  • Publication Date: 2009
  • Doi Number: 10.1016/j.autcon.2008.07.004
  • Journal Name: AUTOMATION IN CONSTRUCTION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.145-152
  • Keywords: Concrete, Accelerated corrosion, Impressed voltage test, Artificial neural network, Computer modeling, ARTIFICIAL NEURAL-NETWORKS, COMPRESSIVE STRENGTH, FUZZY-LOGIC, STEEL FIBER, PREDICTION, RESISTANCE, INHIBITOR, DURABILITY, MORTAR
  • Eskisehir Osmangazi University Affiliated: Yes

Abstract

In this study, the mechanical properties of concretes are determined and the corrosion performances of steel that is embedded in concrete are analyzed by impressed voltage test. Different types of cements are used to prepare the concrete specimens with 0, 10, 20% fly ash. Corrosion currents of each specimen are measured and collected in five minute intervals using a data logger. The corrosion currents are modeled using feed forward artificial neural networks (ANNs). Measured results are then compared with the modeled ones in terms of root mean square error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient criterion. It is concluded that using composite cement or fly ash instead of cement, the durability of concrete against the effects of corrosion is improved considerably. It is also concluded that using ANNs. accurate modeling results for corrosion currents can be obtained. (C) 2008 Elsevier B.V. All rights reserved.