ABSTRACT
The major effects of creep in concrete bridge structures can be summarized in three categories: camber and deflection, stress redistribution, and prestress loss. Creep affects the settings of bearings and the size of sliding plates or laminated bearing pads as well as the size and setting of expansion joints. Creep also influences the redistribution of forces in certain structures where the static system changes during construction, and therefore, plays a major role in stress redistribution for composite construction. Moreover, creep affects the amount of girder shortening due to the prestress and the corresponding loss of prestress, thereby also affects the secondary moments in a prestressed girder bridge. Many factors including water-cement ratio, aggregate-cement ratio, cement content, type of cement, compressive strength, loading age, volume-surface ratio, temperature, relative humidity and stress may affect the long-term creep of concrete. This paper aims to investigate the main factors affecting the long-term creep of concrete using machine learning. For that, multiple regression models for predicting creep coefficient (Linear Model, Ridge Model, Lasso Model, Decision Tree Model, Bagging Model, Random Forest Model, Generalized Boosting Model and Extreme Gradient Boosting Model) are developed using the statistical software R. The creep tests used in this study are from the Northwestern University database. An evaluation of the regression models is performed by comparing the predicted value to the experimental values, and the factors affecting creep are discussed. The results show that ensemble trees give a more accurate prediction of the creep coefficient at long-term and that the factors have different degrees of importance for the various regression models which must be well controlled to avoid the consequences of inaccurate prediction of creep.
