Schematic cost estimating model for super tall buildings using a high-rise premium ratio
Bibliographic record
Abstract
Super tall building construction involves considerable financial uncertainty due to its potentially low returns despite high investments. To reduce this financial risk, it is crucial to accurately estimate the schematic construction cost of such projects. However, traditional cost estimating practices (TCEP) are not effective at predicting the cost of schematic design phase design alternatives that involve the change in the number of building stories. To address these issues, this research proposes a schematic cost estimating model (SCEM). The SCEM estimates the schematic construction cost of super tall building alternatives using a simulation mechanism that considers variation in the number of building stories (i.e., ±5, ±10, ±15, ±20 stories). First, the limitations of the traditional practices are identified. Then, three pilot alternatives (i.e., one schematic design and two design alternatives) are designed and estimated in detail. Next, cost simulation mechanism is constructed based on the relationships between design scale, material quantity, unit cost rate, and construction cost. In addition, after determining which dominant factors affect construction cost when the number of building stories changes, the high-rise premium ratio and its theoretical framework are introduced. This ratio is used to identify the productivity ratios of super tall buildings and to simulate construction cost as the building design changes. Finally, the SCEM is validated through a case study of an actual super tall building. It is found that schematic construction cost increases as the unit cost rate rises due to a low productivity ratio in the case of a higher number of building stories. Conversely, this cost decreases as the unit cost rate goes down due to a high productivity ratio in the case of a lower number of building stories. Ultimately, the SCEM is developed to support effective decision-making during the schematic design phase.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".