Simulation of concrete structures deformation affected by alkali–silica reaction considering environmental conditions and multiaxial stress state
Bibliographic record
Abstract
In this study, the expansion and deformation of alkali–silica reaction (ASR)-affected concrete structures under natural environment and multiaxial stress state are predicted. The performance of the ASR model proposed in previous research is quantitatively investigated in terms of relative humidity, multiaxial stress, and temperature dependencies based on experimental results. The simulation results indicate that the relative humidity-dependent expansion caused by the ASR can be simulated effectively by slightly modifying the relative humidity threshold in the model. In a simulation focusing on stress dependency, the insufficient consideration of the relationship between compressive stress and ASR gel absorption into pores in the model by previous research resulted in discrepancies between the simulation and experimental results. The performance of the model by previous research in simulating temperature dependency is improved by referring to the relationship between the expansion rate and temperature recorded from specimens exposed to the real environment. An exposure experiment of reinforced concrete (RC) slab on steel girders is simulated using the modified model. The results show that the modified model can reproduce the tendency of three-dimensional deformation of RC slab, while model improvement considering time-dependent and stress-dependent phenomena should be needed for long-term quantitative predictions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".