Shear failure in reinforced concrete beams poses a critical safety issue since it may occur without any prior signs
of damage in some cases. Many of the existing shear design equations for steel fiber reinforced concrete (SFRC)
beams include significant uncertainty due to failure in reflecting the phenomenology of shear resistance accurately.
Given these, adequate reliability evaluation of shear design provisions for SFRC beam is of high significance,
and increased accuracy and minimisation of variability in the predictive model is essential. This
contribution proposes machine learning (ML) based methods - Gaussian Process regression (GPR) and the
Random Forest (RF) techniques - to predict the ultimate shear resistance of SFRC slender beams without stirrups.
The models were developed using a database of 326 experimental SFRC slender beams obtained from previous
studies, utilising 75% for model training and the remainder for testing. The performance of the proposed models
was assessed by statistical comparison to experimental results and to that of the state-of-practice existing shear
design models (fib Model Code 2010, German guideline, Bernat et al. model). The proposed ML-based models are
in close alignment with the experimentally observed shear strength and the existing predictive models, but
provide more accurate and unbiased predictions. Furthermore, the model uncertainty of the various resistance
models was characterised and investigated. The ML-based models displayed the lowest bias and variability, with
no significant trend with input parameters. The inconsistencies observed in the predictions by the existing shear
design formulations at the variation of shear span to effective depth ratio is a major cause for concern; reliability
analysis is required. Finally, partial resistance safety factors were proposed for the model uncertainty associated
with the existing shear design equations