Finite Element Modeling and Statistical Analysis of Fire-Damaged Reinforced Concrete Columns Repaired Using Smart Materials and FRP Confinement

Hussain I., Yaqub M., Mortazavi M., Ehsan M. A., Uzair M.

10th International Conference on Fibre-Reinforced Polymer (FRP) Composites in Civil Engineering (CICE), İstanbul, Turkey, 8 - 10 December 2021, vol.198, pp.101-110 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 198
  • Doi Number: 10.1007/978-3-030-88166-5_8
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.101-110
  • Keywords: Fire-damaged columns, Smart construction materials, Numerical modeling, Regression modeling, Abaqus, SPSS, STRENGTH, BEHAVIOR
  • Istanbul Technical University Affiliated: No


Practical testing for observing the failure modes is expensive, time consuming and often limits the pace of research progress. Moreover, because of the limitations of equipment, the existing structures cannot be tested at ultimate failure and scale effect is not observed on large scale experimentally. Thus, numerical modeling and statistical analysis is required for prediction of the behavior of fire damaged and fire-damaged repaired structures. This Paper explores the regression models and Finite element studies to predict the load-deformation response of bolstered concrete columns, damaged through exposure to heat at 300 degrees C, 500 degrees C and 900 degrees C, strengthened using smart materials and confined by carbon fiber reinforced polymers. Using software ABAQUS, a numerical model was developed, capable of predicting the axial load-deformation performance of undamaged, fire damaged and fire-damaged reinforced columns repaired by employing various confinement techniques. SPSS Software was used for Regression modeling (Linear, multiple, and Quadratic). The obtained results showed that regression equations and numerical modeling offered a better alternative to the experimental methods. High correlation coefficient r and coefficient of determination r(2), more than 90%, for all developed equations, confirmed it as an excellent fit statistical model for prediction of axial load capacity and axial deformation. Similarly, the response predicted by numerical modeling showed minor difference i-e less than 10% with that of experimental. Thus, it can be concluded that, the numerical modeling and prediction formulae agree quite with the experimental results and can be used as alternatives for prediction of loads and deformations.