Structural Engineering and Mechanics

  • 제84권5호
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  • Pages.651-664
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  • 2022
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

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Gaussian models for bond strength evaluation of ribbed steel bars in concrete

  • Prabhat R., Prem (CSIR-Structural Engineering Research Centre) ;
  • Branko, Savija (Micro Lab., Faculty of Civil Engineering and Geosciences, Delft University of Technology)
  • 투고 : 2022.07.25
  • 심사 : 2022.10.28
  • 발행 : 2022.12.10
⟨ 이전 논문 다음 논문 ⟩

초록

A precise prediction of the ultimate bond strength between rebar and surrounding concrete plays a major role in structural design, as it effects the load-carrying capacity and serviceability of a member significantly. In the present study, Gaussian models are employed for modelling bond strength of ribbed steel bars embedded in concrete. Gaussian models offer a non-parametric method based on Bayesian framework which is powerful, versatile, robust and accurate. Five different Gaussian models are explored in this paper-Gaussian Process (GP), Variational Heteroscedastic Gaussian Process (VHGP), Warped Gaussian Process (WGP), Sparse Spectrum Gaussian Process (SSGP), and Twin Gaussian Process (TGP). The effectiveness of the models is also evaluated in comparison to the numerous design formulae provided by the codes. The predictions from the Gaussian models are found to be closer to the experiments than those predicted using the design equations provided in various codes. The sensitivity of the models to various parameters, input feature space and sampling is also presented. It is found that GP, VHGP and SSGP are effective in prediction of the bond strength. For large data set, GP, VHGP, WGP and TGP can be computationally expensive. In such cases, SSGP can be utilized.

키워드

과제정보

Authors would like to extend their gratitude to the anonymous reviewer whose valuable comments have helped to improve the quality of the manuscript.

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