Identification of Critical Factors Influencing Early Age Creep of High Strength Concrete Using Artificial Neural Networks
Bibliographic record
Abstract
Ensuring precise estimation of creep characteristics is critical for the efficient design of structures prone to creep deformation.Creep is an indicator of permanent and gradual deformation that transpires within a material when it is continuously subjected to a burden for an extended duration.In addition to the temperature and duration of load application, the degree of creep in concrete is also affected by the material's inherent properties and the length of time it has been exposed to the load.The enduring consequences of creep might result in significant distortions that give rise to structural deficiencies inside the edifice.Multiple factors, including the concrete's mixing ratios and compressive strength, affect the initial hardening of the material.The data pertaining to the early creep behaviour of high-strength concrete were gathered from relevant literature sources for the purposes of this study.The research utilised artificial neural network methodology and relative importance analysis to ascertain the most influential parameters on the early creep behaviour of high-strength concrete.Understanding the components that contribute to creep is crucial in mitigating the detrimental effects of creep on concrete and concrete 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".