Tensile mechanical response of basalt TRM composites exposed to elevated temperatures
Bibliographic record
Abstract
The tensile behaviour of textile-reinforced mortar (TRM) composites and their effectiveness in reinforcingstructures in high-temperature environments remain unknown. This area lacks research because it is a relatively new field, and research efforts have initially focused on understanding their behaviour at ambient temperatures. This study investigated the tensile performance of basalt TRM composites (BTRM) made with short glass fibres at temperatures ranging from 21 to 4000C.Characterisation of the basalt textile (grid) at 21-2000C was also carried out. Samples were heated for one hour at constant temperatures in a furnace, allowed to cool naturally, and then tested at room temperature. Tensile tests were performed with a universal testing machine, and the digital image correlation technique was employed to measure elongation. Elevated temperatures generally led to degradation of the BTRM mechanical properties, including reduced tensile strength, modulus of elasticity, and ultimate strain. At 4000C, the composite had severely deteriorated mechanical functionality. The grid retained at least 90% of its room temperature tensile strength up to 200°C. However, it could not be tested at higher temperatures due to the loss of structural form. The performance and durability of TRM and its constituents need to be improved for use in high-temperature applications beyond 2000C.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".