Bearing capacity prediction of the concrete pile using tunned ANFIS system

The design process for pile foundations necessitates meticulous deliberation of the calculation pertaining to the bearing capacity of the piles. The primary objective of this work was to investigate the potential use of Coot bird optimization ( CBO CBO ) techniques in predicting the load-bearing capacity of concrete-driven piles. Despite the availability of several suggested models, the investigation of Coot bird optimization ( CBO CBO ) for estimating the pile-carrying capacity has been somewhat neglected in this research. This work presents and validates a unique approach that combines the Coot bird optimization ( CBO CBO ) model with the Multi-layered perceptron ( MLP MLP ) neural network and adaptive neuro-fuzzy inference system ( ANFIS ANFIS ). The findings of 472 different driven pile static load tests were put in a database. The proposed framework's building, validation, and testing stages were each accomplished utilizing the training set (70%), validation set (15%), and testing set (15%) of the dataset, respectively. According to the findings, MLP_CBO MLP CBO and ANFIS_CBO ANFIS CBO both offer remarkable possibilities for accurately predicting the pile-bearing capacity of a given structure. The R² R 2 values for ANFIS_CBO ANFIS CBO during the training stage were 0.9874, while during the validating stage, they were 0.9785, and during the testing stage they were 0.987. After considering various kinds of performance studies and contrasting them with existing literature, it has been concluded that the ANFIS_CBO ANFIS CBO model provides a more appropriate calculation of the bearing capacity of concrete-driven piles.