Statistical Process Control of Fiber-Reinforced Concrete Precast Tunnel Segments
Bibliographic record
Abstract
Statistical process control (SPC) procedures are proposed to improve the production efficiency of precast concrete tunnel segments.Quality control test results of more than 1000 ASTM C1609/C1609M beam specimens were analyzed.These specimens were collected over 18 months from the fiber-reinforced concrete (FRC) used for the production of precast tunnel segments of a major wastewater tunnel project in the Northeast United States.The Anderson-Darling (AD) test for the overall distribution indicated that the data are best described by a normal distribution.The initial residual strength parameter for the FRC mixture, f D 600 , is the most representative parameter of the post-crack region.The lower 95% confidence interval (CI) values for 28-day flexural strength parameters of f 1 , f D 600 , and f D 300 exceeded the design strengths and hence validated the strength acceptability criteria set at 3.7 MPa (540 psi).A combination of run chart, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) control charts successfully identified the out-of-control mean values of flexural strengths.These methods identify the periods corresponding to incapable manufacturing processes that should be investigated to move the processes back into control.This approach successfully identified the capable or incapable processes.The study also included the Bootstrap Method to analyze standard error in the test data and its reliability to determine the sample size.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".