Applying computational intelligence methods to evaluate lateral load capacity for a pile

Pile foundations are often subjected to lateral pressures in addition to axial loads due to earthquakes, ground pressure, and wind pressures in various buildings. As a result, pile foundations have gotten more research than any other type of foundation. Furthermore, estimating the lateral load capacity of a pile (LLCP) accurately is a difficult undertaking, and there has been relatively little study in this field. To overcome these problems, in this study, the adaptive neuro-fuzzy inference system (ANFIS) was ustilized to construct forecasting models for the indirect assessment of LLCP embedded in clay in this work. The fuzzy c-means clustering technique (FCM) and the subtractive clustering method (SCM) were implemented as ANFIS models. The data from open-source literature were used to evaluate the two ANFIS models. In these models, pile length (L), pile diameter (D), undrained shear strength of soil (S-u), and eccentricity of load (e) were used as the inputs, while the measured LLCP in clay was the output. To compare the performance of the estimating models, several statistical performance measures were used. The modeling results show that the relationships determined for estimating the LLCP in clay by ANFIS models (ANFIS-SCM and ANFIS-FCM) are accurate and close to the real value. It can also be concluded that the use of ANFIS models to predict the LLCP in clay is very efficient.