Screening design and construction technologies of skyscrapers
Bibliographic record
Abstract
Purpose – The paper aims to study screening design and construction technologies of skyscrapers. Skyscraper projects provide an illustration of important driving factors (e.g. economies of scale and international expertise) when utilising a wide range of solutions, including innovative ones, in the design and construction of building systems and subsystems. The need exists for a methodology for the speedy screening and comprehensive evaluation of candidate solutions covering the complete spectrum of systems that comprise a building project and that have an impact on life cycle performance. Presented in this paper is a three-step evaluation framework directed at meeting this need, along with application of the first step to three case studies performance. Design/methodology/approach – Research objectives were achieved by an extensive literature review of the current state-of-the-art evaluation tools and criteria; formulation of a three-step evaluation process for screening and ranking candidates; identification and structuring of comprehensive checklists of evaluation criteria; application of the first step of the evaluation framework to three case studies to gauge completeness and ease of use; and assessment of the framework by experienced practitioners. Findings – The framework proposed provides a structured and transparent approach to assessing design/construction choices. It makes explicit the spectrum of criteria to be considered when assessing their feasibility. Feedback from industry professionals indicates that the framework is reflective of industry needs. Originality/value – The originality and value of the approach lies in the comprehensiveness of the criteria considered, their relevance to signature building projects that draw on international expertise and technologies and their relevance to all phases of the project life cycle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".