A predictive analytics model for designing deep underground foundations using artificial neural networks

Abstract

Artificial neural networks (ANNs) are commonly used to solve nonlinear problems. ANNs are composed of parallel interconnected layers that include processing elements called neurons. These neurons use mathematical functions to process the data, mimicking the human brain. In this study, we use ANNs for designing deep underground supports. We develop a predictive model for the ultimate axial capacity of deep foundations called drilled shafts, commonly used as support for highways, bridges and high-rise buildings. Different single hidden layer ANN architectures are utilized to predict the ultimate axial capacity of drilled shafts. The data used in this study are load tests collected from the extended version of the Nevada Deep Foundation Load Test Database. This study focuses on certain difficulties in foundation engineering problems, such as the variability in the soil or construction method which, conventional methods might not fully address. This can sometimes lead to overestimation or underestimation of the capacity. Consequently, a project can be unsafe or expensive. Therefore, the objective of this study is to improve prediction accuracy which is important and can save design and construction costs and time. A total of 45 load tests were divided into 85% for training, and 15% for validation. The developed ANN model achieved good generalization and prediction accuracy with a Root Mean-Squared Error of 2807.08 Kilopounds force (kips), a Mean Absolute Error of 2380.6 kips, and an R-squared (R2) of 87% on unseen data. The results can be adapted in future studies and the industry as a first-order estimate for the architecture of an ANN predictive model and the ultimate nominal axial capacity of drilled shafts.