Effect of coarse aggregate concentration on the bond properties between steel rebar and concrete within low and medium strength range

Sidar Nihat Bicakci*, Hasan Nuri Turkmenoglu, Servan Baran, Osama Abo Kaf, Mahsa Farshbaf Maherian, Hakan Nuri Atahan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Most of the current rules and practices employ medium and high-strength concrete with ribbed rebar. However, there are still many structures that are of low concrete class with non-standard aggregate gradation and also contain plain rebar. In this study, to better understand the behavior of such structures, concrete mixtures with 3 different water-to-cement (W/C) ratios (0.6–0.9–1.2) and 4 different coarse aggregate concentrations (CAC) (0 %-20 %-40 %-60 %) were produced. Compressive strength (CS), elasticity modulus (MOE), and splitting tensile strength (STS) tests were performed on the concrete samples, and the stress-strain relationship was determined. Bond-slip behavior was examined on samples containing both plain and ribbed rebars. The results showed that 40 % CAC mixtures had the highest CS and 60 % CAC mixtures had the highest MOE. Adding coarse aggregate increased STS, bond strength (τu), and residual bond stress (τr). The results obtained from this study were also combined with a similar study conducted on medium and high-strength concrete, and models were proposed for CS, MOE, and τu, τr. It was determined that similar to compressive strength, the MOE was also effective on bond strength significantly. A machine learning method (random forest regression) and feature importance analysis were performed to support the results. The results revealed that CS has the most significant effect on τu and τr, but the effect of MOE and STS was also very significant. Additionally, τru and the s1 (slip value at τu), which is proposed as a constant value in existing studies, increased with increasing CS.

Original languageEnglish
Article number138525
JournalConstruction and Building Materials
Volume449
DOIs
Publication statusPublished - 25 Oct 2024

Bibliographical note

Publisher Copyright:
© 2024 Elsevier Ltd

Keywords

  • Bond strength
  • Bond-slip behavior
  • Machine learning
  • Pull-out test
  • Random forest regression method

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