Radial Basis Function to Predict the Maximum Surface Settlement Caused by EPB Shield Tunneling

This paper presents a method to predict the maximum surface settlement caused by EPB shield tunneling using Artificial Neural Network (ANN) based on Radial Basis Function (RBF). Maximum surface settlement above a tunnel due to a tunnel construction is predicted with the help of input variables. A MATLAB based radial basis network model is developed, trained and tested with data on ground deformation and shield operation which were collected through Bangkok MRTA project. The settlement is taken as a function of tunnel depth, distance from launching station, ground water level from tunnel invert, face pressure, penetration rate, pitching angle, tail void grouting pressure and percent tail void grout filling. The output variable is maximum surface settlement. Combining the extensive computerized database and knowledge of what influence the surface ettlements, RBF can become a more useful predictive method compare to using Multi-Layer Perceptron (MLP) based network to predict the surface settlement.

Keywords:

Radial Basis Function , EPB Tunneling , Settlement