15 km TBM exploratory tunnel excavation in the construction Lot H33 of the Brenner Base Tunnel

Lussu, Andrea; Grüllich, Sebastian; Kaiser, Christian; Fontana, A.

doi:10.1002/geot.201900039

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…Der kontinuierliche Vortrieb des Erkundungsstollens (EKS) mittels einer Offenen Gripper TBM-O (Bild 8) zwischen der Nothaltestelle Innsbruck (EKS km 6,9+22.688) und der Vortriebs-Losgrenze zum Baulos H51 (EKS km 23,5+50.140) wurde im September 2015 begonnen und im Juli 2020 beendet [3].…”

Section: Erkenntnisse Aus Dem Vortrieb Des Erkundungsstollens Im Baul...unclassified

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Brenner Basistunnel, Baulos H41 Sillschlucht–Pfons: Herausforderungen aufbauend auf den Vorlosen im betroffenen Projektbereich

Insam,

Mössmer,

Radončić

et al. 2023

Geomechanics and Tunnelling

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The construction lot H41 Sill Gorge–Pfons is one of the biggest construction lots of the Brenner base tunnel and carries out the work on the Austrian side between Austria and Italy. It is situated between the Sill Gorge in Innsbruck and the small mountain village of Pfons. Here, tunnel excavation and inner lining concrete works are carried out based on existing caverns and tunnels made by previous construction lots E41, Sill Gorge and H33 Tulfes–Pfons. The construction lot H41 Sill Gorge–Pfons started in January 2022 with preparation works, and will take 80 months of continuous construction work to complete. All together about 22.5 km of main tubes, 38 bypasses and the remaining parts of the underground Innsbruck emergency station/stop will be excavated by the drill and blast method and by mechanised means (TBM). Additionally, concrete inner lining works will be executed partly alongside the excavation works. In the connection tunnels which are already finished and link the existing Innsbruck bypass with the new Brenner base tunnel, barrier walls are being erected and a new long‐distance water flushing system is being implemented. The limited surface areas in this mountainous region present a huge challenge for logistics and storage as well as on‐site operations for the execution of this complex underground construction lot.

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Section: Erkenntnisse Aus Dem Vortrieb Des Erkundungsstollens Im Baul...unclassified

“…The mechanical drive of the exploratory tunnel (EKS) using an open-gripper TBM (Fig. 8) between the Innsbruck emergency stop (EKS km 6.9+22.688) and boundary with the H51 (EKS km 23.5+50.140) construction lot started in September 2015 and was complete in July 2020 [3].…”

Section: Insights Gained From the Tunnel Drive Of The Exploratory Tun...mentioning

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Brenner Basistunnel, Baulos H41 Sillschlucht–Pfons: Herausforderungen aufbauend auf den Vorlosen im betroffenen Projektbereich

Insam,

Mössmer,

Radončić

et al. 2023

Geomechanics and Tunnelling

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Grouped machine learning methods for predicting rock mass parameters in a tunnel boring machine‐driven tunnel based on fuzzy C‐means clustering

Wang,

Ni,

Zhang

et al. 2024

Deep Underground Science and Engineering

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To guarantee safe and efficient tunneling of a tunnel boring machine (TBM), rapid and accurate judgment of the rock mass condition is essential. Based on fuzzy C‐means clustering, this paper proposes a grouped machine learning method for predicting rock mass parameters. An elaborate data set on field rock mass is collected, which also matches field TBM tunneling. Meanwhile, target stratum samples are divided into several clusters by fuzzy C‐means clustering, and multiple submodels are trained by samples in different clusters with the input of pretreated TBM tunneling data and the output of rock mass parameter data. Each testing sample or newly encountered tunneling condition can be predicted by multiple submodels with the weight of the membership degree of the sample to each cluster. The proposed method has been realized by 100 training samples and verified by 30 testing samples collected from the C1 part of the Pearl Delta water resources allocation project. The average percentage error of uniaxial compressive strength and joint frequency (Jf) of the 30 testing samples predicted by the pure back propagation (BP) neural network is 13.62% and 12.38%, while that predicted by the BP neural network combined with fuzzy C‐means is 7.66% and 6.40%, respectively. In addition, by combining fuzzy C‐means clustering, the prediction accuracies of support vector regression and random forest are also improved to different degrees, which demonstrates that fuzzy C‐means clustering is helpful for improving the prediction accuracy of machine learning and thus has good applicability. Accordingly, the proposed method is valuable for predicting rock mass parameters during TBM tunneling.

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Real-time hard-rock tunnel prediction model for rock mass classification using CatBoost integrated with Sequential Model-Based Optimization

Liu

Huang

et al. 2022

Tunnelling and Underground Space Technology

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15 km TBM exploratory tunnel excavation in the construction Lot H33 of the Brenner Base Tunnel

Cited by 3 publications

References 3 publications

Brenner Basistunnel, Baulos H41 Sillschlucht–Pfons: Herausforderungen aufbauend auf den Vorlosen im betroffenen Projektbereich

Brenner Basistunnel, Baulos H41 Sillschlucht–Pfons: Herausforderungen aufbauend auf den Vorlosen im betroffenen Projektbereich

Grouped machine learning methods for predicting rock mass parameters in a tunnel boring machine‐driven tunnel based on fuzzy C‐means clustering

Real-time hard-rock tunnel prediction model for rock mass classification using CatBoost integrated with Sequential Model-Based Optimization

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