A novel hybrid intelligent model for the prediction of creep coefficients based on random forest and support vector machine.

A low deviation creep coefficient prediction model is vital for engineering practice related to soft clays. However, current empirical methods for calculating creep coefficients are not sufficiently reliable. This study presents a new hybrid intelligent model that couples random forest (RF) and support vector machine (SVM) by weighting for prediction of creep coefficient of soft clays. The genetic algorithm (GA) is used to search the optimum hyperparameters of the proposed model. A total of 151 datasets collected from the literature were used to construct the proposed model. Limited by the size of the dataset, k-fold cross-validation is used in the optimization processes. The obtained results show that the forecasting performances of the proposed model surpass those of the empirical methods, recommended for the prediction of creep coefficient in practical engineering. In addition, for the convenience of practical engineering application, we have built a web page program for the proposed hybrid model, and the users can use the model by visiting the website. • A hybrid intelligent model coupling random forest and support vector machine was proposed to predict the creep index. • Genetic algorithm was used to search the optimum hyperparameters of the proposed models. • Robustness and parametric analysis were conducted to verify the reasonableness of the hybrid model. [ABSTRACT FROM AUTHOR]