Estimating wall deflections in deep excavations using Bayesian neural networks

Abstract

In this study, a neural network algorithm has been used to model the soil-structure interaction behavior of deep excavations in clays. The hybrid evolutionary Bayesian back-propagation (EBBP) neural network was used in this study and utilizes the genetic algorithms and gradient descent method to determine the optimal parameters within a Bayesian framework to regularize the complexity of learning and to statistically reflect the uncertainty in data. The EBBP analysis was carried out on an extensive database of braced excavation performance from finite element analyses. Additional parametric studies indicate that the model gives logical and consistent trends. Back-analyses of some instrumented case histories from the literature also indicate that the trained neural network model gives reasonable predictions in comparison to the actual measured values. The trained model can serve as a simple and reliable prediction tool to enable estimates of maximum wall deflection for preliminary design of braced excavations in clay. The model is able to take into consideration various factors such as the wall stiffness, support stiffness, the in-situ stress state, non-homogeneous soil conditions, and the variation of soil properties with depth. An added advantage of this approach is that it provides meaningful error bars for the model predictions.