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.

中文翻译：

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遗传规划与人工神经网络在桩基中的应用综述

岩土材料（例如土壤和岩石）行为的不确定性是与它们形成有关的物理过程不精确的结果。这种不确定性为建模此类材料的行为提供了复杂性。相同条件适用于处理结构元素的行为。在这方面，桩基作为用于将上部结构荷载传递到地下的结构要素，受到这些材料的不确定性和建模复杂性的影响。与传统方法相比，人工智能（AI）在建模材料的复杂行为方面表现出了卓越的预测能力。这种能力使AI在岩土工程应用中成为一种流行且特别适合的选择。遗传编程（GP）和人工神经网络（ANN）是AI技术最常见的两个例子。本文综述了GP和ANN在估计桩基础承载力中的应用。