Proposal of poro-mechanical coupling among ASR, corrosion and frost action for damage assessment of structural concrete with water
Bibliographic record
Abstract
Concrete durability related events have similar processes in terms of crack initiation and its propagation such as alkali silica reaction (ASR), freeze-thaw cycles (FTC) and steel corrosion. All of them will create additional liquid/solid pore substances and finally lead to volumetric expansion and subsequent cracking in concrete composites. This paper builds a platform of triple poro-mechanical coupling model for micro and meso-scale events of ASR, FTC and steel corrosion in consideration of the mutually interacting processes. The proposed model attempts to cover the most essential aspects, from thermo-chemo coupling at micro-scale to the poro-mechanical coupling at meso-scale, and finally leads to the coupled fracture of structural concrete at macro-scale. Several simulation examples are presented for both single and coupled deteriorations of structural concrete, and it is clarified that the combined deformation and damage of concrete are not the simple compilation of each sole effect, but it is highly path-dependent on both crack patterns and deformation levels. This triple coupling model can provide a platform, on which the coupled complex damages to structural concrete are consistently dealt with for damage assessment of structural concrete.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".