Hydromechanical couplings of reinforced tensioned members of steel fiber reinforced concrete by dual lattice model
Bibliographic record
Abstract
Abstract The durability of concrete structures strongly depends on the water and chloride penetration in cracked concrete during its service life. This work aims at modeling the damage effect of tension stiffening behavior on the permeability for concrete tie specimen under tensile load by a dual lattice model, which considers hydromechanical couplings. Three concrete materials, including normal strength concrete (NSC), steel fiber reinforced concretes (SFRC), and ultra high‐performance fiber reinforced concrete (UHPFRC), are considered. The hydromechanical lattice model is based on a dual element network modeling: the water transport and the mechanical response. The fiber bridging effect is considered by means of the cohesive law of Mazars. The water flow in the damaged conduit elements is proportional to the cube of the crack width, which results from the damage variable. Experimental results available in open literature for both NSC and SFRC tie specimens are used to analyze and validate the proposed model. Considering a UHPFRC tie specimen, the model well predicted the load drops due to macrocrack occurrence, load hardening, and permeability evolution. Based on the present results, the current lattice hydromechanical model is a useful tool for predicting the service life of steel bar reinforcing concrete structure with and without steel fiber reinforcement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.002 | 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".