Bond stress estimation of profiled steel-concrete in steel reinforced concrete composite structures using ensemble machine learning approaches

Abstract

The bond-slip behavior between profiled steel and concrete is a critical issue in the steel reinforced concrete (SRC) composite structures. Existing models for predicting the bond strength of profiled steel–concrete were obtained based on small test data sets and may not provide accurate predictions when applied more broadly. To this end, this paper presents a data-driven analysis on the bond stress of profiled steel–concrete using ensemble machine learning approaches. A database of 249 individual tests in terms of bond stresses at different working conditions (i.e., bond stress at initial slip, ultimate bond stress, and residual bond stress) associating geometries, stirrup and profiled steel characteristics and concrete property were compiled from available literature. Based on the constructed database, six existing empirical models of ultimate bond stress were evaluated and the assessment results demonstrate that these models cannot achieve a desirable accuracy for their predictions. Then four independent ensemble machine learning algorithms were employed to develop the prediction models of bond stresses. The hyper-parameters of the developed models were optimized using Bayesian optimization method in order to improve their prediction performance. Finally, the prediction results of the ensemble machine learning-based models were interpreted using SHapley Additive exPlanations (SHAP) and feature importance analysis. The comprehensive comparisons demonstrate that jointly adopting ensemble machine learning and Bayesian optimization approaches can estimate the bond stress of profiled steel–concrete with high predictability and interpretability.