Design–information technology and performances in building and industrial projects
Bibliographic record
Abstract
This paper describes a collaborative effort by industry, government, and academia to evaluate the use of design-information technology (D/IT) in building and industrial projects and to relate the degree of use to project performance. A detailed statistical analysis of 566 projects in the Construction Industry Institute (CII) database is used to produce baseline measures of performance and D/IT use. The relationship between these measures is used to assess the economic value of using the technologies. The results of this study establish that projects benefit from D/IT use. Owners experienced project cost savings of 2.1% and contractors experienced cost savings of 1.8% as D/IT use increased. Construction schedule growth was reduced by 5.6% for owners and by 6.4% for contractors with increased D/IT use. Impacts on safety performance are less clear: owners obtain quantifiable safety benefits, whereas for contractors, the impact was minimal. The statistical analyses also confirm that reductions in project development, scope changes, and field rework are associated with increased D/IT use.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".