Application of fuzzy logic approach in predicting the lateral confinement coefficient for RC columns wrapped with CFRP

Doran B., Yetilmezsoy K., Murtazaoğlu S.

ENGINEERING STRUCTURES, vol.88, pp.74-91, 2015 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 88
  • Publication Date: 2015
  • Doi Number: 10.1016/j.engstruct.2015.01.039
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.74-91
  • Keywords: Artificial intelligence, Concrete column, Fiber reinforced polymer, Fuzzy logic, Lateral confinement coefficient, STRESS-STRAIN MODEL, ARTIFICIAL NEURAL-NETWORK, CONCRETE COLUMNS, JACKETED CONCRETE, PERFORMANCE EVALUATION, COMPRESSIVE STRENGTH, ULTIMATE STRENGTH, FRP, DESIGN, SHEAR
  • Yıldız Technical University Affiliated: Yes


Worldwide ageing infrastructures which are vulnerable to seismic lateral loads and located in high seismicity regions have arrested the interest of many researchers to find alternative materials and techniques to strengthen in bending and shear, for example reinforced concrete (RC) beams, slabs, columns, etc. There are several strengthening/repair techniques and materials in literature. Although the method of strengthening concrete structures with fiber reinforced polymers (FRP) is a relatively new technique, it has existed for more than two decades. In this context, several confinement models have been developed for FRP-confined concrete for the prediction of stress-strain response and several researchers have developed various constitutive models to measure the increase in the axial strength of concrete dueto the confinement effect of FRP laminates. In this study, RC columns wrapped with carbon FRP (CFRP) considering some existing confinement models in the literature have been investigated. Moreover, based on the experimental data set in the literature, a new artificial intelligence-based algorithm (a Mamdani-type fuzzy inference system) was implemented to model the strength enhancement of CFRP confined RC columns using fuzzy logic methodology. Fuzzy logic predicted results were compared with the outputs of a non-linear regression analysis-based exponential model derived in the scope of the present work. The best predictive performances of the models were assessed by means of various descriptive statistical indicators. The comparison of the proposed prognostic approach with existing empirical and experimental data exhibits a very good precision of the developed artificial intelligence-based model in predicting the lateral confinement coefficient in CFRP wrapped RC columns. (C) 2015 Elsevier Ltd. All rights reserved.