Load-settlement modeling of axially loaded steel driven piles using CPT-based recurrent neural networks

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 design of pile foundations should thus consider the bearing capacity and settlement inseparably. This requires the full load-settlement response of piles to be well predicted. However, it is well known that the actual load-settlement response of pile foundations can only be obtained by load tests carried out in-situ, which are expensive and time-consuming. In this technical note, the recurrent neural networks (RNNs) were used to develop a prediction model that can resemble the load-settlement response of steel driven piles subjected to axial loading. The developed RNN model was 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 developed RNN model has the ability to reliably predict the load-settlement response of axially loaded steel driven piles and can thus be used by geotechnical engineers for routine design practice.