Predictive Analytics Toward Optimal Tunnel Boring Machine Operation

Project Description

Modern tunnel boring machines are capturing a wide range of operating and system condition parameters, including speed, drive torque, cutting wheel contact pressure and the support pressure in the excavation chamber. Typically, this data is used for control purposes.

The goal of this project is to use machine learning to develop new applications from the available data. First, we explore how to use the data to obtain more information on the geology surrounding the machine during its operation. This information should allow the operator to adapt the settings of the machine accordingly. Second, we propose to learn from historic data the sets of operating parameters that lead to efficient tunneling. The aim is to develop a recommendation system for the operators, such that they can always operate in the most efficient regime.

This project particularity is the strong intertwining between first, the geological conditions, second, the consequences on the operation of the machines and on the wear of the tools, and last, the expertise of the operator, who adapt the operation accordingly. In addition, the large uncertainty on the geology profile, only partially inferred from prior explorations, makes the validation of the models extremely challenging.