Statistical Evaluation of Groutability Using Data from Hydraulic Tests and Fracture Mapping Case Studies from Sweden
Bibliographic record
Abstract
Sweden has a long history of research within the field of rock fissure grouting in hard crystalline rock mass due to strict environmental requirements regarding allowable ground water draw down. These requirements normally implies that fractures down to aperture size between 50 to 100 µm needs to be sealed and within these ranges the size of the particles for cementitious grouting agents becomes a limiting factor. For a grouting design it is therefore of importance to consider the aperture size distribution of the rock mass in order to predict the groutability for both cementitious and non-cementitious grouting agents. Transmissivity data from hydraulic tests (water pressure tests) and number of fractures along a borehole can be assessed from core logging for further use as input for a statistical interpretation of fracture data to simulate an aperture size distribution. A methodology developed at Chalmers University of Technology in Gothenburg, Sweden, is proposed. The method is a statistical evaluation of groutability (SEG) and is based on the Pareto distribution. A computational design tool has been developed to simplify the use of the statistical evaluation and to make the research more accessible to end users, designers, in the grouting industry. The aim of this article is to present two case studies where the statistical interpretation of fracture data is performed by using the computational design tool and how the outcome can be of great use in finding a more accurate grouting design. The case studies include fracture data sets from two large infrastructure rock tunnel projects in Sweden; a road tunnel in Stockholm and a railroad tunnel in Gothenburg.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".