Prediction of shear strength in UHPC beams using machine learning-based models and SHAP interpretation

To provide more accurate and reliable predictions of the shear strength of ultrahigh-performance concrete (UHPC) beams, in this study, the machine learning (ML) approaches were employed to develop the data-driven models, and the ML models were interpreted using the Shapley additive explanations (SHAP) method. It was found that the ensemble models, particularly CatBoost, outperform individual ML models and traditional empirical models. The geometric dimensions and shear span-to-depth ratio were the most influential features for predicting the shear strength of UHPC beams, followed by the parameters of reinforcement and material properties of the UHPC.

Automated summary

In this study, machine learning approaches were used to develop data-driven models for predicting the shear strength of ultrahigh-performance concrete beams. The results showed that ensemble models, especially CatBoost, outperformed individual ML models and traditional empirical models. Geometric dimensions and shear span-to-depth ratio were the most influential features for predicting the shear strength.