Assessment of Settlement Profile Caused by Underground Box Structure Installation with an Artificial Neural Network Model

Abstract

Various empirical equations have been proposed to predict the ground settlement profile caused by the excavation of conventional circular tunnels. However, ground movement for the underground box structure has not been fully studied. In this study, ground settlement induced by underground box installations is investigated using two-dimensional finite element analyses. A new formulation to assess the settlement profile applicable to underground box structure is proposed based on parametric analyses of the changes in ground condition, geometric condition of structure, and construction conditions. This paper also presents a method to predict the maximum surface settlement around an underground box structure with artificial neural networks (ANNs), taking into account nine input variables that have direct physical significance. A MATLAB-based multi-layer back propagation neural network model is developed, trained, and tested with parameters obtained from numerical analyses. The maximum settlement from the ANN model, in conjunction with a new formulation to construct the settlement profile, turns out to be promising, by predicting a settlement profile compatible with field measurement data.