Characterization of UHPFRC Materials for Bridge Construction: An Opportunity to Offset the Need for Prestressing in Bridge Decks
Bibliographic record
Abstract
In the current decade, an increasing number of reinforced concrete bridge structures are deemed to be in need of repair either due to the short durability lifetime of the materials of their original design, or due to climatic extremes that cause the concrete materials to fail (Mermigas 2018). This is especially true for structures such as bridges which are continuously exposed to variations of the environment including temperature, moisture, and road de-icing salts (Carl 1971, L. Spellman 1971, Demich 1975, Steinkamp 2015). \n\nIn the present work, a detailed research study of a Canadian UHPFRC material product is undertaken with the objective to investigate the material performance to determine its durability life and identify the limitations and challenges the prequalification process and Code Guidelines present when used to characterize the material. Further, the applicability of the Annex A8.1 was explored of the design procedures using a design benchmark example. The exciting finding of this investigation concluded that by taking advantage of the mechanical properties of this material, it is possible to produce design alternatives for large bridge spans without prestressing (Lee et al. 2017). To quantify the global effects on the response of the structural components comprising UHPFRC materials, design parameters affecting the flexural response of UHPFRC girders were studied.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".