GA-SVR: a novel hybrid data-driven model to simulate vertical load capacity of driven piles

Piles are widely applied to substructures of various infrastructural buildings. Soil has a complex nature; thus, a variety of empirical models have been proposed for the prediction of the bearing capacity of piles. The aim of this study is to propose a novel artificial intelligent approach to predict vertical load capacity of driven piles in cohesionless soils using support vector regression (SVR) optimized by genetic algorithm (GA). To the best of our knowledge, no research has been developed the GA-SVR model to predict vertical load capacity of driven piles in different timescales as of yet, and the novelty of this study is to develop a new hybrid intelligent approach in this field. To investigate the efficacy of GA-SVR model, two other models, i.e., SVR and linear regression models, are also used for a comparative study. According to the obtained results, GA-SVR model clearly outperformed the SVR and linear regression models by achieving less root mean square error (RMSE) and higher coefficient of determination (R-2). In other words, GA-SVR with RMSE of 0.017 and R-2 of 0.980 has higher performance than SVR with RMSE of 0.035 and R-2 of 0.912, and linear regression model with RMSE of 0.079 and R-2 of 0.625.

Automated summary

The study proposed a novel artificial intelligent approach using genetic algorithm optimized support vector regression to predict the vertical load capacity of driven piles in cohesionless soils. The results showed that the new GA-SVR model outperformed traditional models, indicating its potential application in structural engineering design.