Experimental Investigation and Estimation of Bond Strength of Rebar and High-Performance Fiber-Reinforced Cementitious Composites under High Temperature

Document Type : Original Article

Authors

1 Ph.D. Student, Department of Civil Engineering, University of Guilan, Rasht, Iran

2 Associate Professor, Department of Civil Engineering, University of Guilan, Rasht, Iran

3 Professor, Department of Civil Engineering, University of Guilan, Rasht, Iran

4 Associate Professor, Department of Civil Engineering, University of Mazandaran, Babolsar, Iran

Abstract

In this study, the bond strength between rebar and high-performance fiber-reinforced cementitious composites (HPFRCC) containing the combination of three types of steel fibers (1% and 2%), polypropylene and polyvinyl alcohol (0.1, 0.2, 0.3 and 0.4%) has been investigated at the temperature of the laboratory, 400 and 600 °C. For this purpose, at first, 19 specimens were constructed and evaluated for compressive strength testing under the mentioned temperatures, as the silica fume was considered 20% by weight of cement. Among the constructed HPFRCC, four superior mix designs were selected for investigating the bond strength between rebar and HPFRCC using pullout test. The results showed that the bond strength between rebar and HPFRCC samples containing 2% steel fibers with PP fiber in such a way that increasing the temperature up to 400 °C, decreased about 38%. while this reduction rate for the samples containing PVA fibers is about 26%, and this means that PVA fibers have a better performance than PP fibers in term of the bond between concrete and rebar when exposed to high temperatures. By increasing the temperature up to 600°c, the bond strength of rebar and HPFRCC continues to decrease until this drop is about 64% for selected samples containing fibers (2% steel and 3% PP) at the laboratory temperature (i.e., 23°c). The reduction for the HPFRCC sample containing 2% steel and 2% PVA fibers is calculated by 62%. The results of this study and the literatures indicated the effect of different parameters on the bond strength, so for further investigation, the bond strength modelled using artificial intelligence models. The results of rebar bond strength modeling in HPFRCC showed that the performance of the adaptive multivariate regression splines based on error statistical criteria was more accurate than the artificial neural network.

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