Prediction of Lateral Confinement Coefficient in Reinforced Concrete Columns using M5’ Machine Learning Method

Predicting the lateral confinement coefficient in reinforced concrete columns is a very important issue in structural engineering. Therefore, several experimental formulas have developed to predict it. Recently, soft computing tools such as artificial neural networks have been used to predict the confinement coefficient. However, these tools are not as transparent as empirical formulas. In this study, another soft computing approach, i.e. model trees have been used for predicting the confinement coefficient. The main advantage of model trees is that, unlike the other data learning tools, they are easier to use and more importantly they represent understandable mathematical rules. In this paper, a new formula that includes some structural parameter is derived using dimensionless parameter for estimating the confinement coefficient. A comparison is made between the estimated confinement coefficient by this new formula and formula given by others researches shows the accuracy of prediction.