Automatic Classification of Geology Using TBM Data
For the optimization of TBM driving parameters, the complementation of the geological tunnel documentation, and
the minimization of risk in zones of highly fractured rock,
a computer-based system that is able to automatically
make decisions on the type of geological conditions is
desirable. We propose a classification system that correlates sensor data of the TBM with geological assessments
in order to provide such an estimation.
The system relies on a machine learning method from
statistical pattern recognition. By using Bayes classification and techniques for the approximation of probability
density functions, an estimation of the geological class-membership of a new TBM data set can be derived from
the statistical model.
The system learns the structure of the data adaptively,
and without a priori usage of domain- or expert knowledge, which makes it employable for a wide range of TBM
tunnel projects. Recent experiments showed that a classification system based on this method is appropriate for
practical application. Our approach is currently being realized as an additional software component of the tunnel
documentation system 2doc by Pöyry Infra GmbH Salzburg.
Helmut A. Mayer
Last modified: Nov 14 2008