Towards a Uniform Definition of Rock Toughness for Penetration Prediction in TBM Tunneling

Wilfing, Lisa; Käsling, Heiko; Thuro, Kurosch

doi:10.1007/978-3-319-09060-3_82

Cited by 6 publications

(13 citation statements)

References 4 publications

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“…The Thrust Penetration Gradient (TPG) represents the nonlinear energy demand for the mining process of TBMs. This concept, initially developed by Wild and Weh et al and adopted by Wilfing, Erharter et al and Wannenmacher, focuses on understanding the interaction between related mechanical aspects and geotechnical conditions [10][11][12]. Wilfing simplified Weh et al and Wild's approach by skipping the lower trust plateau, instead implementing a linear slope for the remaining thrust increase as the torque grows non-linearly.…”

Section: Determination Of the Friction Forcementioning

confidence: 99%

Advancing TBM Performance: Integrating Shield Friction Analysis and Machine Learning in Geotechnical Engineering

Schlicke,

Wannenmacher,

Nübel

2024

Geotechnics

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The Ylvie model is a novel method towards transparent Tunnel Boring Machine (TBM) data analysis for tunnel construction. The model innovatively applies machine learning to automate friction loss computation per stroke, enhancing TBM performance prediction in varying geomechanical environments. This research considers the complexities of TBM mechanics, focusing on the Thrust Penetration Gradient (TPG) and shield friction influenced by geological conditions. By integrating operational data analysis with geological exploration, the Ylvie model transcends traditional methodologies, allowing for a comprehensible and specific determination of the friction loss towards more precise penetration rate prediction. The model’s capability is validated through comparative analysis with established methods, demonstrating its effectiveness even in challenging hard rock tunneling scenarios. This study marks a significant advancement in TBM performance analysis, suggesting potential for the expanded application and future integration of additional data sources for comprehensive rock mass characterization.

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Section: Determination Of the Friction Forcementioning

confidence: 99%

Advancing TBM Performance: Integrating Shield Friction Analysis and Machine Learning in Geotechnical Engineering

Schlicke,

Wannenmacher,

Nübel

2024

Geotechnics

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“…A second aspect of the Ph.D. project of Lisa Wilfing is the uniform definition of rock toughness (commonly known as Z coefficient = UCS/BTS [22]) combining factor k 1 (factor for specific fracture energy) and k 2 (factor for rock mass fabric) in Eqn 4. As the existence of a post-failure range as well as a high plastic deformation in the pre-failure range are main characteristics of tough rocks [15], the ratio of destruction work W z to failure energy W B as well as the elastic strain ε el to strain at failure ε u ratio are illustrated in Fig. 3 and correlated with the Z coefficient.…”

Section: Further Developments 31 Rock Toughness Classificationmentioning

confidence: 99%

Hard rock TBM Tunnelling – on the way to a better prognosis? / TBM‐Hartgesteinsvortriebe: Auf dem Weg zu einer verbesserten Prognose?

Thuro

Wilfing

Wieser

et al. 2015

Geomechanics and Tunnelling

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Five universities from Austria, Germany and Switzerland, working intensively together with partners from the industry, client organisations, companies, consultants and TBM manufacturers, have combined to develop a new prognosis model for TBM advance rate and disc wear (the "ALPINE MODEL"). This article presents some new results from the project. Fundamentally, the new model is to be a development of the model from Gehring, which calculates the penetration from the ratio of disc load to uniaxial compression strength. Since this equation was derived from data collected from a limited number of projects in the 1980s, it is of great importance to widen the data base and to establish a standardised process for this purpose. For on-site tests, the "Start-stoppenetration test" is considered the most efficient and suitable method. This test can be carried out with the TBM in the course of tunnelling and thus results in little delay. In addition to the recording of TBM data during the penetration test, detailed geological documentation of the face and the tunnel sides has to be undertaken, accompanied by geomechanical laboratory tests. A large number of such penetration tests have now been carried out on various tunnel projects in order to validate and improve the basic formula. Some of the first results are described in this article. Further themes include the rock mechanical parameters to be used, the influence of the primary stress conditions at the face and the abrasiveness of the rock. Fünf Universitäten aus Österreich, Deutschland und der Schweiz, die intensive mit Partnern aus der Wirtschaft, Bauherren, Firmen, Ingenieurbüros und TBM Herstellern zusammenarbeiten, entwickeln gemeinsam ein neues Prognosemodell für die TBM Vortriebsleistung und den Rollenmeißel-Verschleiß (das "ALPINE MODEL"). In diesem Artikel werden einige neue Ergebnisse dazu vorgestellt. Grundsätzlich soll das neue Modell wie das zugrunde liegende Modell von Gehring aufgebaut sein, dessen Basis aus der Berechnung der Penetration durch das Verhältnis von Diskenlast und Einaxialer Druckfestigkeit besteht. Nachdem diese Beziehung aus Daten abgeleitet ist, die in 1980er-Jahren aus einer beschränkten Anzahl von Projekten gesammelt wurden, ist es von unmittelbarer Bedeutung, diese Datenbasis auszuweiten und dafür ein Standardverfahren zu etablieren. Für Vor-Ort-Versuche wird der sog. "Start-Stop-Penetrationsversuch" als effizienteste und geeignetste Methode angesehen. Dieser Versuch kann mit der TBM während des Vortriebs durchgeführt werden und führt daher nur zu sehr geringen Vortriebsverzögerungen. Neben der Aufzeichnung der TBM Vortriebsdaten während des Penetrationsversuchs muss eine detaillierte geologische Dokumentation der Ortsbrust und der Laibung sowie felsmechanische Laboruntersuchungen der Gesteine vorgenommen werden. Eine große Anzahl von solchen Penetrationsversuchen wurde bei unterschiedlichen Tunnelprojekten durchgeführt, um die Basis-Formel zu validieren bzw. zu verbessern. Einige erste Ergebnisse werden in diesem Artikel vorgestellt. An...

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“…Viele Jahre stand die Prognose der Penetration im Vordergrund der Forschung, doch durch entsprechen de Schadensfälle und sich anschließende Auseinanderset zungen zwischen Auftraggebern und Auftragnehmern ist der Verschleiß der Abbauwerkzeuge seit einigen Jahren immer mehr in den Fokus gerückt. Doch nicht nur die Vortriebsleistung wird vom Werkzeugverschleiß beein flusst, sondern folglich auch die Bauzeit und die Baukos ten, denn Stillstände zur Wartung oder Reparatur der Werkzeuge reduzieren die Verfügbarkeit des Gesamtsys tems, und der Austausch von Werkzeugen erzeugt direkte Material und Lohnkosten [1][2][3][4].…”

Section: Introductionunclassified

“…Whereas formerly the pre diction of penetration was the main focus of research, due to relevant damage claims and subsequent disputes between clients and contractors, the emphasis has in creasingly shifted to tool wear in recent years. Yet tool wear has an influence not only on the advance rate, but, as a consequence, on the construction time and costs as well, since downtimes for tool maintenance or repairs reduce the availability of the overall system and tool replacement generates direct material and labour costs [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

State of the Art der Verschleißprognose im maschinellen Tunnelbau

Stopp,

Feinendegen

2023

Geomechanics and Tunnelling

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The wear of cutting tools in mechanised tunnelling is closely linked to the abrasiveness of the present subsoil. Therefore, most of the models used for wear prediction take an abrasiveness index into account. The most common index used for hard rock is the Cerchar Abrasiveness Index (CAI); whereas such a standard does not yet exist for soil. Both the literature research and the results of calculation examples presented in the article show that significantly more practical models are currently available for wear prediction in hard rock. One reason for this is that far more wear‐relevant influencing factors have to be quantified for soft ground. Another reason is that the scientific studies concerning this topic only started in the course of mechanised tunnelling. Since especially the calculations carried out for low‐abrasive rocks differ widely among the different models for tool life, further research is needed also for wear prediction in hard rock

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Towards a Uniform Definition of Rock Toughness for Penetration Prediction in TBM Tunneling

Cited by 6 publications

References 4 publications

Advancing TBM Performance: Integrating Shield Friction Analysis and Machine Learning in Geotechnical Engineering

Advancing TBM Performance: Integrating Shield Friction Analysis and Machine Learning in Geotechnical Engineering

Hard rock TBM Tunnelling – on the way to a better prognosis? / TBM‐Hartgesteinsvortriebe: Auf dem Weg zu einer verbesserten Prognose?

State of the Art der Verschleißprognose im maschinellen Tunnelbau

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