Bibliographic record
Abstract
This thesis presents a computer model for quantifying the adverse impact of change orders on construction productivity. In order to provide an in-depth analysis of change orders and develop a reliable model, a comprehensive field study was carried out. The field study was conducted at a Montreal based firm, specialized in project management and construction claims. A total of 117 actual projects, constructed in Canada and the USA between 1990 and 1998, were initially analyzed for possible use in the developments made in this thesis. Only 33 work-packages from these projects were utilized in the development of the present model. These work packages have an original total value of more than $110M, planned direct hours of 1,023,583 for the original scope of work and a total of change orders direct hours of 166,002. Additional cases, obtained from the literature, were used to supplement the collected data in order to improve the reliability of the developed model. The analyzed cases are used to model the timing effect of change orders as well as the work type on productivity losses. The data collected was used in the development of ten neural network models for predicting percent productivity loss. (Abstract shortened by UMI.)
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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".