Brenner Base Tunnel exploratory tunnel Ahrental‐Pfons – interaction between tunnel boring machine and rock mass as well as measures to manage fault zones

Flora, Matthias; Grüllich, Sebastian; Töchterle, Andreas; Schierl, Heimo

doi:10.1002/geot.201900044

Cited by 2 publications

(4 citation statements)

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“…TBM specifics vary between manufacturers, one example on data logged during the operation of an Herrenknecht open gripper TBM is presented in this chapter. On this machine the roof support shield is driven by two independently movable left-and right cylinders [26]. Sensors separately record the pressure that acts on both sides of the TBM's roof support shield.…”

Section: Exemplary Section Of Tbm Data Between Chainage 2000 and 2750 M; Several Features Show A Distinctive Response To The Encountered mentioning

confidence: 99%

Capabilities and Challenges Using Machine Learning in Tunnelling

Marcher¹,

Erharter²,

Unterlaß³

2022

Theory and Practice of Tunnel Engineering

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Digitalization changes the design and operational processes in tunnelling. The way of gathering geological data in the field of tunnelling, the methods of rock mass classification as well as the application of tunnel design analyses, tunnel construction processes and tunnel maintenance will be influenced by this digital transformation. The ongoing digitalization in tunnelling through applications like building information modelling and artificial intelligence, addressing a variety of difficult tasks, is moving forward. Increasing overall amounts of data (big data), combined with the ease to access strong computing powers, are leading to a sharp increase in the successful application of data analytics and techniques of artificial intelligence. Artificial Intelligence now arrives also in the fields of geotechnical engineering, tunnelling and engineering geology. The chapter focuses on the potential for machine learning methods – a branch of Artificial Intelligence - in tunnelling. Examples will show that training artificial neural networks in a supervised manner works and yields valuable information. Unsupervised machine learning approaches will be also discussed, where the final classification is not imposed upon the data, but learned from it. Finally, reinforcement learning seems to be trendsetting but not being in use for specific tunnel applications yet.

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Section: Exemplary Section Of Tbm Data Between Chainage 2000 and 2750 M; Several Features Show A Distinctive Response To The Encountered mentioning

confidence: 99%

Capabilities and Challenges Using Machine Learning in Tunnelling

Marcher¹,

Erharter²,

Unterlaß³

2022

Theory and Practice of Tunnel Engineering

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show abstract

“…Gained knowledge is then transferred to the main tunnels, as they are driven by shield TBMs with limited insights on the geology surrounding the tunnels Bergmeister 2019). For the excavation of the ET a hard-rock gripper TBM with a cutterhead diameter of 7.93 m has been used, amongst others equipped with a roof shield with independently movable right-and left cylinders which indirectly indicate rock load variations (Flora et al 2019). These shields are extended against the overlying rock mass during TBM non-advance periods (TBM standstills), to provide protection for the machinery and the personal in the first four free standing meters behind the tunnel face (Figs.…”

Section: Bbt Exploratory Tunnel Ahrental-pfons and Tbm Specificsmentioning

confidence: 99%

“…The main foliation of both lithological units dips roughly to the West with varying inclinations between flat and steep. Looking in advance direction of the tunnel, the main foliation is therefore mostly dipping to the right (Flora et al 2019).…”

Section: Rock Mass Conditionsmentioning

confidence: 99%

“…Outside larger fault zones, the system behaviour (OEGG 2013) on the one hand is influenced by the spacing and orientation of other discontinuities such as faults and fissures and on the other hand by the orientation and arrangement of the foliation. Flora et al 2019 stated that between these structural elements, the following two relationships can be observed:…”

Section: Rock Mass Conditionsmentioning

confidence: 99%

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Identifying Rock Loads on TBM Shields During Standstills (Non-Advance-Periods)

2022

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Tunnel boring machine (TBM) operational data is mostly analysed with respect to data that was recorded during the advance of the TBM. Focusing on data that was recorded during standstills of a gripper TBM, we analyse rock loads that were passively recorded in the cylinders of a small roof support shield. These roof support cylinders are situated beneath the TBM’s shield – extending it against the rock mass during non-advance periods. Equipped with pressure sensors, they enable the unique opportunity of logging rock load variations throughout the tunnel. Hence due to the big amount of resulting data, techniques of unsupervised machine learning (i.e. cluster analysis) are used to automatically pre-process the TBM operational data. Furthermore, regression analysis is used to determine sections of the tunnel where rock loads are mainly occurring on the left or right side respectively. The data driven analysis shows that the main rock loads are occurring on the right side of the TBM which is in good accordance with observation from the construction site, as well as numerical models from literature. This paper contributes towards the understanding of rock load conditions in anisotropic rock masses recorded during the drive of a deep hard rock tunnel.

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Brenner Base Tunnel exploratory tunnel Ahrental‐Pfons – interaction between tunnel boring machine and rock mass as well as measures to manage fault zones

Cited by 2 publications

References 2 publications

Capabilities and Challenges Using Machine Learning in Tunnelling

Capabilities and Challenges Using Machine Learning in Tunnelling

Identifying Rock Loads on TBM Shields During Standstills (Non-Advance-Periods)

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