A case study of a partially pre-stressed multi-storey building incorporating fast-track construction
Bibliographic record
Abstract
This paper presents a case study of the construction of a partially pre-stressed multi-storey office block in Adelaide, Australia. The designers and construction managers faced numerous issues involving the construction speed and performance of the nine storey building, in particular the performance of the two-way partially pre-stressed suspended slabs. The planned construction period was an absolute minimum due to a contract for early occupancy and high daily costs of construction equipment. Excessive deflections of the 9.6 m × 8.4 m × 200 mm thick slabs were a concern for this fast-track construction. This situation created some significant issues to ensure satisfactory construction speed and acceptable deflection levels. This paper discusses these issues while also explaining and documenting the steps taken to obtain the final solution. A large collection of data from the construction process is presented. An emphasis is placed on the material properties of the concrete slabs, namely compressive strength (fc) and modulus of elasticity (Ec) development over time, due to their direct effect on the construction timeline. The effects of concrete curing conditions on material property development are also addressed. Lastly the outcomes and performance of the slabs, namely construction time and slab deformations, are presented.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".