Artificial intelligence for modelling load-settlement response of axially loaded bored piles - Shahin, Mohamed (CRC Press, Taylor and Francis Group, 2014)
The design of pile foundations requires reliable estimation of the pile load-carrying capacity and settlement. Design for bearing capacity and design for settlement are 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 inseparably. This requires the full load-settlement response 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 full-scale static load tests carried out in-situ, which are expensive and time-consuming. In this paper, artificial intelligence (AI) using the recurrent neural networks (RNNs) is used to develop a prediction model that can resemble the full load-settlement response of bored 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 reliably predict the load-settlement behavior of axially loaded bored piles and can thus be used by geotechnical engineers for routine design practice.