A review of Genetic Programming and Artificial Neural Network applications in pile foundations

Abstract Uncertainty in the behavior of geotechnical materials (e.g. soil and rock) is the result of imprecise physical processes associated with their formation. This uncertainty provides complexity in modeling the behavior of such materials. The same condition is applied to the behavior of the structural elements dealing with them. In this regard, pile foundations, as the structural elements used to transfer superstructure loads deep into the ground, are subjected to these material uncertainties and modeling complexity. Artificial Intelligence (AI) has demonstrated superior predictive ability compared to traditional methods in modeling the complex behavior of materials. This ability has made AI a popular and particularly amenable option in geotechnical engineering applications. Genetic Programming (GP) and Artificial Neural Network (ANN) are two of the most common examples of AI techniques. This paper provides a review of GP and ANN applications in estimation of the pile foundations bearing capacity