Using GA-ANFIS machine learning model for forecasting the load bearing capacity of driven piles

Abstract

This paper is aimed to apply hybrid machine learning model namely GA-ANFIS, which is a combination of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA), for the prediction of total bearing capability of driven piles. A database of 95 Pile Driving Analyzer (PDA) tests carried out at the win power project in Hoa Binh province, Vietnam was used to develop hybrid model. The database was split into 70:30 ratio for training (70%) and validating (30%) model. Accuracy of the model was evaluated using statistical standard indicators: Coefficient of determination (R2), Mean Absolute Error (MAE), and Root mean squared error (RMSE). Results indicated that the GA-ANFIS model has a good performance in correct prediction of the total bearding capability of driven piles on both training (R2 = 0.976) and testing (R2 =0.925) datasets. Therefore, the GA-ANFIS hybrid model is a promising tool for quick and accurate prediction of the total bearing capability of driven piles for the consideration in design and construction of the structures.