A rock-machine-muck multi-source information fusion method for tunnel boring machine tunneling parameter control

Abstract

The selection of tunnel boring machine (TBM) tunneling parameters predominantly relies on manual experience, which hinders the achievement of optimal control of the tunneling process. This study proposes a new control method that integrates multi-source information from the rock mass, the machine, and the muck to enhance tunneling performance while ensuring construction safety, thereby providing a scientific basis for TBM control. The proposed methodology comprises three key stages. First, a multi-source heterogeneous engineering database is constructed by systematically collecting TBM tunneling parameters, muck parameters, and rock mass parameters. Subsequently, two models are developed: a rock mass grade identification model and a tunneling performance prediction model, both based on the tunneling parameters and muck characteristics from the TBM’s thrust-raising phase. These models are designed to identify the rock mass grade at the tunnel face and predict key performance parameters during the stable tunneling phase, respectively. Finally, by integrating the model outputs with a statistical characterization of engineering data, a graded control strategy tailored to different rock mass grades is formulated. Engineering application verification demonstrates that the proposed method achieves robust adaptability and effective control performance. This work provides a theoretical framework and practical guidance for auxiliary intelligent TBM tunneling in complex geological conditions.

Graphical abstract

Keywords

TBM / multi-source information fusion / rock mass identification / performance prediction / parameter control

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