Prediction of tunneling-induced ground movement with the multi-layer perceptron

This paper presents a method to predict ground movement around tunnels with artificial neural networks. Surface settlement above a tunnel and horizontal ground movement due to a tunnel construction are predicted with the help of input variables that have direct physical significance. A MATLAB based multi-layer backpropagation neural network model is developed, trained and tested with parameters obtained from the detailed investigation of different tunnel projects published in literature. The settlement is taken as a function of tunnel diameter, depth to the tunnel axis, normalized volume loss, soil strength, groundwater characteristics and construction methods. The output variables are settlement and trough width. Parameters for the prediction of horizontal ground movement include diameter to depth ratio (D/Z), unit weight of soil and cohesion. The neural network demonstrated a promising result and predicted the desired goal fairly successfully.