Social Housing: Critical Evaluation of Prestress Losses in Precast Slabs
Bibliographic record
Abstract
Prestressed concrete involves artificially inducing controlled stresses by means of high-tensile steel cables in the opposite direction to the stresses caused by applied loads.These compressions, superimposed on the tensile stresses induced by applied loads, create a total stress state within limits that concrete can withstand indefinitely.Prestressing does have drawbacks, such as stress losses, which are strategically managed.Delayed losses due to concrete creep are evaluated using the formula:Stress losses due to steel relaxation are evaluated using the expression: ∆𝑓𝑓 𝐶𝐶𝐸𝐸 = 𝐶𝐶[𝐾𝐾 𝑐𝑐𝑟𝑟 -𝐽𝐽(∆𝑓𝑓 𝐸𝐸𝐸𝐸 + ∆𝑓𝑓 𝐶𝐶𝐶𝐶 + ∆𝑓𝑓 𝐸𝐸𝑆𝑆 )]The objective is to assess the stress losses in both steel and concrete to ensure the structural operational life.It was found that the losses due to concrete shrinkage and steel relaxation are small, particularly when they are deferred.There is a need to distinguish between transfer sections and critical sections, tentatively located at a distance of l/4 from the support.In the case of the PPCC:6/60:30 type precast slab, the only one where, due to wire eccentricity, a convex deflection may occur upon the transfer of prestressing.It is reported that the result is valid for the industrialisation of the PPCC model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".