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TECHNOLOGY AND CODE article

Front. Earth Sci.
Sec. Geohazards and Georisks
Volume 12 - 2024 | doi: 10.3389/feart.2024.1518844

Classification and prediction of rock mass drillability for a tunnel boring machine based on operational data mining

Provisionally accepted
Mingshe Sun Mingshe Sun 1Song Chen Song Chen 2*Huafei He Huafei He 3Wenzheng Wang Wenzheng Wang 3Kezhi Song Kezhi Song 1Xuebing Lin Xuebing Lin 3
  • 1 Ludong University, Yantai, Shandong Province, China
  • 2 Hebei GEO University, Shijiazhuang, China
  • 3 Beijing Gaoxin Municipal Engineering Technology Co., Ltd, Beijing, China

The final, formatted version of the article will be published soon.

    Currently, the accurate prediction of tunnel boring machine (TBM) performance remains a considerable challenge due to the complex interactions between the TBM and rock mass.In this study, the research work is based on part of a metro tunnel project that covers 2083.94m.The Gaussian mixture model (GMM) and K-nearest neighbor algorithm (KNN) are used to classify and predict the rock mass drillability in the TBM excavation process. Drillability indexes are introduced to cluster and classify the rock mass, including the penetration (P), field penetration index (FPI), torque penetration index (TPI), and specific energy (SE). Statistical characteristics of the drillability indexes were analyzed, and it was found that their distributions did not conform to the normal distribution, with large variation coefficients. Clustering analysis was then conducted on the TPI and FPI within the training group using the Gaussian mixture model, and six drillability categories of rock mass were classified. Subsequently, the mapping relationship between cutterhead speed, advance speed, total advance force, and cutterhead torque in the training group and the drillability of rock mass was established based on the KNN classification model. It was revealed that when the K is set to 4, the model has high macro-F1, macro-P and macro-R.Validated by the testing group data, this method has been proven to be feasible and effective. The research results indicate that this method can effectively classify and predict the drillability of tunneling surrounding rock mass in shield construction, particularly when the rock mass at the shield face is uniform and homogeneous. This provides a theoretical basis and technical support for safe and efficient shield tunneling.

    Keywords: Tunnel boring machine, Rock mass classification, operational data mining, Gaussian mixture model, K-nearest neighbor

    Received: 30 Oct 2024; Accepted: 03 Dec 2024.

    Copyright: © 2024 Sun, Chen, He, Wang, Song and Lin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Song Chen, Hebei GEO University, Shijiazhuang, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.