Model tree approach for predicting uniaxial compressive strength and Young’s modulus of carbonate rocks

The uniaxial compressive strength (UCS) and Young’s modulus (E) of rock are important parameters for evaluating the strength, deformation, and stability of rock engineering structures. Direct measurement of these parameters is expensive, time-consuming, and even infeasible in some circumstances due to the difficulty involved in obtaining core samples. Recently, soft computing tools have been used to predict UCS and E based on index tests. Most of these tools are not as transparent and easy to use as empirical regression-based models. This study presents another soft computing approach—model trees—for predicting the UCS and E of carbonate rocks. The main advantages of model trees are that they are easier to use than other data learning tools and, more importantly, they represent understandable mathematical rules. In this study, the M5P algorithm was employed to build and evaluate model trees (UCS and E model trees). First, the models were developed in an unpruned form, and then they were pruned to avoid overfitting. The data used to train and test the model trees were collected from quarries in southwestern Turkey. Model trees included Schmidt hammer, effective porosity, dry unit weight, P‐wave velocity, and slake durability index as input variables. When the models were assessed using a number of statistical indices (RMSE, MAE, VAF, and R2), it was found that unpruned and pruned model trees provide acceptable predictions of UCS and E, although the pruned models are simpler and easier to understand.