Artificial intelligence for modeling load-settlement response of axially loaded (steel) driven piles

Abstract

The design of pile foundations requires good estimation of the pile load-carrying capacity and settlement. Design for bearing capacity and design for settlement have been traditionally carried out separately. However, soil resistance and settlement are influenced by each other and the design of pile foundations should thus consider the bearing capacity and settlement in-separately. This requires the full load-settlement behavior of piles to be well predicted. However, it is well known that the actual load-settlement behavior of pile foundations can only be obtained by load tests carried out in-situ, which are expensive and time-consuming. In this paper, artificial intelligence (AI) using the recurrent neural networks (RNN) is used to develop a prediction model that can resemble the full load-settlement response of steel driven piles subjected to axial loading. The developed RNN model is calibrated and validated using several in-situ full-scale pile load tests, as well as cone penetration test (CPT) data. The results indicate that the RNN model has the ability to predict well the load-settlement response of axially loaded steel driven piles and can thus be used by geotechnical engineers for routine design practice.