Estimation of bond strength between UHPC and reinforcing bars using machine learning approaches

Bond strength estimation plays an important role in structure engineering. This paper proposes to adopt machine learning approaches to conduct a data-driven analysis of bond strength between ultra-high performance concrete (UHPC) and reinforcing bars. To make up for the lack of experimental data, a new database is established by integrating 557 instances from several published works. A total of nine machine learning models which can be divided into three types are implemented to train the bond strength estimators based on the database, including linear models, tree models, and artificial neural networks. Four strong metrics, i.e. Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Ratio of Accurate Estimation (RACC), are used to evaluate the performance of models. Among them, Artificial Neural Network and Random Forest achieve great estimation performances in the top two, which far exceed the empirical formulas. They have 74% and 73% of estimated data to keep the relative error within 10%, respectively. The statistical relative importance of different factors from tree models consistently shows that the ratio of embedded depth to the diameter of reinforcing bars has a significant impact on the bond strength of UHPC, which is conformable with the observations in experiments.