ANFIS Models with Subtractive Clustering and Fuzzy C-Mean Clustering Techniques for Predicting Swelling Percentage of Expansive Soils

Civil engineering faces significant challenges from expansive soils, which can lead to structural damage. This study aims to optimize subtractive clustering and Fuzzy C-Mean Clustering (FCM) models for the most accurate prediction of swelling percentage in expansive soils. Two ANFIS models were developed, namely the FIS1S model using subtractive clustering and the FIS2S model utilizing the FCM algorithm. Due to the MATLAB graphical user interface's limitation on the number of membership functions, the coding approach was employed to develop the ANFIS models for optimal prediction accuracy and problem-solving time. So, two programs were created to determine the optimal influence radius for the FIS1S model and the number of membership functions for the FIS2S model to achieve the highest prediction accuracy. The ANFIS models have demonstrated their highest predictive ability in predicting swelling percentage, thanks to the optimization of membership functions and cluster centers. The developed programs also showed excellent performance and can be potentially applied to optimize subtractive clustering and FCM models in accurately modeling various engineering aspects.