Specifying and testing fibre reinforced sprayed concrete: Advantages and challenges of some testing methods
Bibliographic record
Abstract
The mechanisms through which FRSC (Fibre Reinforced Sprayed Concrete) controls deformations and absorbs energy are complex and involve the technicalities of both the sprayed concrete process and the fibre properties.Toughness is generally the main criterion for FRSC as ground support. However, for the FRSC to have the highest strength and toughness, there must be an optimal design and combination of concrete strength, fibre anchoring and placement method.Consequently, the choice of fibre type and content must correspond to the ground conditions of a specific area and the expected concrete mixture design. There is no unique design method broadly accepted for the design of FRSC for ground support. General guides exist, but there is no specific/complete design guide accepted for FRSC as ground support.Multiple performance criteria: flexural strength, residual flexural strength after cracking, moment-normal force (M-N) behaviour, energy absorption (toughness). This paper will underline, as mentioned in the European standard EN 14487-1, the different ways of specifying the ductility of FRSC in terms of residual strength and energy absorption capacity. The energy absorption value measured on a panel can be prescribed when - in case of rock bolting - emphasis is put on energy, which has to be absorbed during the deformation of the rock. This is especially useful for primary sprayed concrete linings. The residual strength can be prescribed when the concrete characteristics are used in a structural design model. This paper will present the majors results on a research project undertaken this year at Laval, Roma and Bochum University. The characterization of a fiber reinforced concrete made with EFNARC is similar to what obtained with EN 14651 beam tests.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".