Behaviour and Performance of Monofilament Macro-Synthetic Fibres in Dry-Mix Shotcrete
Bibliographic record
Abstract
The use of monofilament macro-synthetic fibres in wet-mix shotcrete applications has grown significantly worldwide since their introduction in the late 90's. As opposed to the stiffer steel fibres, which have to be used at relatively short lengths (30mm) to reduce line blockage, the more flexible macro-synthetic fibres can typically be used, in well proportioned wet-mix shotcrete mixtures, at lengths ranging from 50 to 100mm, without significantly reducing the pumpability and shootability of the mixture. Other reported benefits of macro-synthetic fibres over steel fibres in wet-mix shotcrete include reduced equipment wear and fibre rebound, and increased shotcrete layer built-up thickness. However, attempts to date to incorporate macro-synthetic fibres in dry-mix shotcrete have failed for numerous technical reasons. The problems encountered range from the inability to uniformly mix the fibres at the dry-batching plant using a standard mixing sequence, to fibres causing conduit blockage of the batching dispensing unit, fibres causing blockage of the dry material exiting from the bulk bags, fibres collecting and blocking the shotcrete gun and lines, very high fibre rebound and consequently, very poor in-situ performance. This paper presents the details and results of a systematic testing program that was conducted to identify the key parameters affecting the performance of monofilament macro-synthetic fibres in dry-mix shotcrete. Based on the results obtained from this study, modifications were made to the geometrical characteristics of a specific fibre type to eliminate the problems observed and enable the production of high quality macro-synthetic fibre reinforced dry-mix shotcrete for commercial use.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".