On the strength prediction in concrete construction based on early age results: Case studies
Bibliographic record
Abstract
800x600 Early prediction of strength is crucial in planning for stripping off the formworks and preventing non-working days in concrete construction projects. There are several empirical correlations which allow estimation of concrete strength from early age results. However these correlations have limitations in application. This study established an experimental database which comprised of 382 datasets of strength tests of ordinary Portland cement concrete. These tests were performed over a period of 8 years as part of QA/QC program on 51 construction projects in the Province of Guilan, Northern Iran. From the data, strength ratios between ages (27 and 8 days), (42 and 7 days), (42 and 14 days), and (42 and 28 days) were analysed. New linear and power relations were proposed for estimating 28- and 42-day strength values. Analyses of relative errors along with cumulative probability approach revealed that three well-known models from literature were inaccurate in prediction of strength. It was found out that a correlation by Slater (1926) over-predicted 28-day strength from 7-day test data. Furthermore, the ACI committee 209 (1997) and CEB-FIP (1990) models under-predicted 42-day strength using 28-day strength results. This research should assist in the global, yet simple, understanding of concrete strength development with age. Normal 0 false false false EN-CA X-NONE AR-SA MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:Table Normal; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:Times New Roman,serif;}
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".