Probabilistic prediction model of steel to concrete bond failure under high temperature by machine learning.

• Probabilistic prediction of concrete-steel (CS) bond strength under high temperature is achieved via Natural gradient boosting. • 267 CS pull-out test samples were gathered to train a prediction model and the model reaches an accuracy of 97%. • The representative deterministic machine learning algorithms and empirical formulas are adopted for comparison. • The mechanism behind the proposed model is explored to prove its rationality and feasibility by Shapley values. The interface bond performance between concrete and steel bars under high temperature has a crucial influence on the fire resistance design of the reinforced concrete (RC) structure, but there is no unified model for the prediction of the bond strength yet. Previous experimental studies have conducted plenty of RC member pull-out tests under high temperature, which could be collected as a comprehensive database. As a data-driven method, machine learning (ML) can efficiently establish the regression relationship between input features and output directly through the data. However, current studies usually use classic ML algorithms for establishing a deterministic prediction model, where only one scalar prediction would be provided and its confidence level is uncertain, which is not instructive for the users. Thus, based on previous experimental data, this paper uses the Natural Gradient Boosting (NGBoost) algorithm to establish a unified probabilistic prediction model for the bond strength between steel bars and concrete under high temperature, considering several key factors, such as fiber fraction, concrete compressive strength, bond strength under room temperature et al. By training on the collected 267 experimental data, the prediction results of the ML based model show that these models attain higher accuracy than those of empirical formulas, and the NGBoost based probabilistic prediction model has better prediction performance than the general deterministic ML models. Finally, the Shapley value method is used to explain the calculation results of the model, and compared with the statistical results such as variance and mean of the calculation results of the five empirical formulas, ML has obvious advantages. [ABSTRACT FROM AUTHOR]