Progressive Excavation Disturbance Zone Evolution During and Post Mine-By Tunneling (PRECODE) – Insight into a New Underground Research Laboratory for Crystalline Rocks in the BedrettoLab
Bibliographic record
Abstract
ABSTRACT: Development of brittle damage around tunnels is quite common in massive rocks under highly-stressed conditions. Understanding the shape and extent of the Excavation Damage Zone (EDZ) is important, particularly for deep geological repositories (DGRs) for nuclear waste. Within the EDZ, permeability is often increased, and in the context of nuclear waste disposal, this increased permeability may create preferred pathways for the migration of radionuclides. Therefore, a comprehensive assessment of brittle fracture formation in space and time requires multidisciplinary monitoring systems. A new Underground Research Laboratory (URL) in Southern Switzerland in the old Bedretto Gallery is under construction for the purpose of studying the evolution of the Excavation Damage Zone (EDZ) in crystalline rocks. The new experimental tunnel will form a new branch from the existing Bedretto tunnel which will be densely instrumented prior to the excavation with various sensors. The main objectives of the PRECODE experiment are to understand: (1) short-term rock mass behavior and EDZ formation during tunneling; (2) long-term fracture propagation within the EDZ associated with environmental conditions (fluctuations in humidity and temperature); (3) permeability evolution around an open excavation and (4) the impact of tunneling on potential dislocations of nearby fault zones. In-situ data and a series of comprehensive laboratory tests form a hydro-seismo-mechanically coupled reference data set for numerical simulations with the aim to further improve predictive models. This paper outlines an overview of the project objectives, details of the planned monitoring systems, and some preliminary results obtained from a baseline study. 1. INTRODUCTION Development of brittle fracturing around tunnels in massive and highly stressed rocks is quite common. In crystalline rocks (i.e. granite), the brittle fracturing is depicted through spalling and in a severe case rock bursting (Diederichs, 2007). The fracture initiation and propagation often start during the tunneling process and develop over time. These progressive failures may lead to an interconnected fracture network typically associated with the radiation of micro-seismic signals. Within the excavation damage zone (EDZ), the permeability is often enhanced and – in the framework of nuclear waste disposal – may provide preferential pathways for radionuclide migration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".