Document Type

Journal Article

Publication Title

Engineering Structures

Publisher

Elsevier

School

School of Engineering

RAS ID

57923

Comments

Karmokar, T., Mohyeddin, A., & Lee, J. (2023). Predictive models for concrete cone capacity of cast-in headed anchors in geopolymer concrete. Engineering Structures, 285, article 116025. https://doi.org/10.1016/j.engstruct.2023.116025

Abstract

The scope of current state-of-the-art prediction models for concrete cone capacity of cast-in headed anchors is limited to normal concrete. In this study, the difference in the tensile performance of cast-in headed anchors embedded in ambient-temperature cured fly ash-based geopolymer concrete and normal concrete is investigated using both experimental and numerical analysis. The concrete cone capacity obtained for anchors investigated in this study is compared with current prediction models namely: Concrete Capacity Design (CCD) model, which overestimated the results by a maximum of 41%, and Linear Fracture Mechanics (LFM), which underestimated the results by a maximum of 53%. Anchors of sizes 1.3T, 2.5T and 5T are tested at effective embedment depths of 40 mm, 70 mm and 90 mm. Finally, based on the experimental results obtained in this study and other research[1],[2], modification factors are suggested to be incorporated in the CCD and LFM models so the application of the two models can be expanded to predict the concrete cone capacity of anchors in geopolymer concrete. The modification factors are validated using over 60 numerical analyses on similar anchors with effective embedment depths ranging between 40 mm and 180 mm. The modified CCD model shows an average numerical-to-prediction ratio of 1.09 and a COV of 7%, whereas, the modified LFM model shows an average numerical-to-prediction ratio of 1.02 and a COV of 3%. This indicates that the proposed modification factors are able to predict the concrete cone capacity of anchors in geopolymer concrete investigated in this study with good accuracy.

DOI

10.1016/j.engstruct.2023.116025

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Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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