Decision Support System for an Intelligent Operator of Utility Tunnel Boring Machines

TL;DR

This paper presents an intelligent decision support system that uses deep learning to optimize TBM control parameters, balancing advance rate and safety in uncertain ground conditions, demonstrated on real micro-tunnelling data.

Contribution

It introduces a novel three-step approach combining performance evaluation, deep learning modeling, and real-time recommendations for TBM operators.

Findings

Effective on real micro-tunnelling project data

Shows promise for improving tunneling efficiency and safety

Provides incremental, data-driven control suggestions

Abstract

In tunnel construction projects, delays induce high costs. Thus, tunnel boring machines (TBM) operators aim for fast advance rates, without safety compromise, a difficult mission in uncertain ground environments. Finding the optimal control parameters based on the TBM sensors' measurements remains an open research question with large practical relevance. In this paper, we propose an intelligent decision support system developed in three steps. First past projects performances are evaluated with an optimality score, taking into account the advance rate and the working pressure safety. Then, a deep learning model learns the mapping between the TBM measurements and this optimality score. Last, in real application, the model provides incremental recommendations to improve the optimality, taking into account the current setting and measurements of the TBM. The proposed approach is evaluated on real micro-tunnelling project and demonstrates great promises for future projects.