Date of Award

Spring 2001

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Civil Engineering

First Advisor

Ray Sterling

Abstract

Microtunneling is a trenchless technology for construction of pipelines. Its process is a cyclic pipe jacking operation. Microtunneling has been typically used for gravity sewer systems in urban areas. Despite its good success record overall, several large ground settlement cases caused by microtunneling have been reported. Also, in contrast with large diameter urban tunneling, there are few research projects about the ground settlement caused by microtunneling.

In this dissertation, the ground settlement caused by microtunneling is studied using a theoretical approach, empirical approach, numerical simulation approach, and artificial intelligence approach.

In the theoretical approach, the equivalent ground loss and settlement caused by concentrated ground loss have been used to drive the ground settlement profile. In the empirical approach, the ground settlement caused by large diameter tunneling case histories is used. In the numerical approach, FLAC 3D software, a commercially available finite difference code, is used to simulate the ground settlement caused by microtunneling. In the artificial intelligence approach, a three-layer back propagation neural network is developed to predict the ground settlement caused by microtunneling using the numerical simulation results.

It is found that the neural network developed as part of this thesis work provides a means of rapid prediction of the surface ground settlement curve based on the soil parameters, project geometry and estimated ground loss. This prediction matches FLAC3D results very well over the full range of parameters studied and has a reasonable correspondence to the field results with which it was compared.

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