Comparative study of an improved prediction model and machine learning methods in predicting the compressive creep of self-compacting concrete

The concrete structure under constant loading generates the creep strain, which directly influences the durability of concrete, especially self-compacting concrete (SCC). This study improved the fib MC2010 model to predict both basic creep and total creep of SCC based on the expanded laboratory database of SCC with a total of 281 data sets of creep function. The improved model, namely the fib-SCC model, exhibited the best efficiency in creep prediction of SCC compared to other models, such as the fib MC2010 model and the other available models of SCC. Additionally, new methods were introduced to predict the creep of SCC based on the machine learning methods, such as decision tree, random forest, extra trees, gradient boosting, and extreme gradient boosting. The results indicated that the gradient boosting and XGBoost methods were more prone than decision tree, random forest, and extra trees methods in overfitting the total creep database of SCC.