Bearing capacity prediction of the concrete pile using tunned ANFIS system

Abstract

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 ($${\text{CBO}}$$ 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 ($${\text{CBO}}$$ 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 ($${\text{CBO}}$$ CBO ) model with the Multi-layered perceptron ($${\text{MLP}}$$ MLP ) neural network and adaptive neuro-fuzzy inference system ($${\text{ANFIS}}$$ 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, $${{\text{MLP}}}_{{\text{CBO}}}$$ MLP CBO and $${{\text{ANFIS}}}_{{\text{CBO}}}$$ ANFIS CBO both offer remarkable possibilities for accurately predicting the pile-bearing capacity of a given structure. The $${R}^{2}$$ R 2 values for $${{\text{ANFIS}}}_{{\text{CBO}}}$$ 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 $${{\text{ANFIS}}}_{{\text{CBO}}}$$ ANFIS CBO model provides a more appropriate calculation of the bearing capacity of concrete-driven piles.