Shear strength prediction for SFRC and UHPC beams using a Bayesian approach

Hae Chang Cho, Min Kook Park, Jin Ha Hwang, Won Hee Kang, Kang Su Kim

Research output: Contribution to journalArticlepeer-review


This study proposes prediction models for the shear strength of steel fiber reinforced concrete (SFRC) and ultra-high-performance fiber reinforced concrete (UHPC) beams using a Bayesian parameter estimation approach and a collected experimental database. Previous researchers had already proposed shear strength prediction models for SFRC and UHPC beams, but their performances were limited in terms of their prediction accuracies and the applicability to UHPC beams. Therefore, this study adopted a statistical approach based on a collected database to develop prediction models. In the database, 89 and 37 experimental data for SFRC and UHPC beams without stirrups were collected, respectively, and the proposed equations were developed using the Bayesian parameter estimation approach. The proposed models have a simplified form with important parameters, and in comparison to the existing prediction models, provide unbiased high prediction accuracy.

Original languageEnglish
Pages (from-to)503-514
Number of pages12
JournalStructural Engineering and Mechanics
Issue number4
StatePublished - 25 May 2020


  • Bayesian parameter estimation
  • SFRC
  • Shear strength
  • Steel fiber
  • UHPC


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