Factors affecting buildability of building designs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
By identifying the succinct attributes of design, the abstract concept of buildability can be expressed in a more defined and tangible way for its improvements. In this research, factor analysis is used to expound the data obtained from a questionnaire survey on buildability attributes. Results show the first three out of nine key buildability factors for building designs are (i) allowing economic use of contractor resources, (ii) enabling design requirements to be easily visualized and coordinated by site staff, and (iii) enabling contractors to develop and adopt alternative construction details. Discriminant analysis has been used to identify significant differences amongst the respondent groups. At the project level, the findings give designers a better understanding of factors affecting the buildability of their outputs, thus enabling design solutions leading to more efficient and safe construction. At the industry level, the identified factors can contribute to sustainable development through the reduction of waste and the economic use of resources.Key words: design appraisal, buildability improvement, factor analysis, discriminant analysis.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it