Automatic Monitoring of Grouting Performance Parameters
Bibliographic record
Abstract
Grout pressure and injected grout volumes are the two critical performance parameters that are commonly considered to evaluate the success of grouting operations. These two parameters have traditionally been measured using direct read devices such as Bourdon gages and flow meters. Direct read devices are however subject to errors and omissions from the operator, do not provide context, and do not provide a record of the grouting process for project documentation. For this reason, attempts at automatic recording of grout pressures and injected grout volumes started as early as in the `80s but the recording systems available at that time were bulky, costly, and usually required proprietary software. Nowadays, instruments and components are available that allow us to build light, portable and reliable grout monitoring systems that can be deployed easily by simple connection to the grout injection circuit. Further, the data can be processed in standard programs such as Microsoft Excel. The paper reports on a simple commercially available Grout Monitoring System that uses standard software. It has been developed and used on numerous permeation and compaction grout injection projects. Features of the system include a low-power data logger that can read pressure and flows at up to 2 second intervals, wireless interface to a field PC for real-time display of grouting parameters, and plotting of grout injection strategies such as GIN and others, including custom-defined strategies. The system can be expanded to suit project requirements such as logging several injection points simultaneously, local and remote display, web display, distribution via FTP sites, and monitoring of additional parameters such as grout density, ground heave or tilt of adjacent structures.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".