In this study, load bearing capacities of closed- and open-ended piles in cohesive and cohesionless soils are predicted using machine learning. Nine such methods are utilized in the analyses where Cone Penetration Test (CPT) and pile data are considered as the learning features necessary to teach those methods the database gathered via a comprehensive search. Then, machine learning models are developed, and the databases are separated into five-folds according to the cross-validation-principle, which are used for both training and testing of the machine learning methods.
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DOI: doi.org/10.1080/19386362.2023.2251795