Thermo‐hydro‐mechanical modelling of high performance concrete at high temperatures
Bibliographic record
Abstract
This paper presents the physical, mathematical and numerical models forming the main structure of the numerical analysis of the thermal, hydral and mechanical behaviour of normal, high‐performance concrete (HPC) and ultra‐high performance concrete (UHPC) structures subjected to heating. A fully coupled non‐linear formulation is designed to predict the behaviour, and potential for spalling, of heated concrete structures for fire and nuclear reactor applications. The physical model is described in more detail, with emphasis being placed upon the real processes occurring in concrete during heating based on tests carried out in several major laboratories around Europe as part of the wider high temperature concrete (HITECO) research programme. A number of experimental and modelling advances are presented in this paper. The stress‐strain behaviour of concrete in direct tension, determined experimentally, is input into the model. The hitherto unknown micro‐structural, hydral and mechanical behaviour of HPC/UHPC were determined experimentally and the information is also built into the model. Two examples of computer simulations concerning experimental validation of the model, i.e. temperature and gas pressure development in a radiatively heated HPC wall and hydro‐thermal and mechanical (damage) performance of a square HPC column during fire, are presented and discussed in the context of full scale fire tests done within the HITECO research programme.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".