Trending and Forecasting in Construction Operations
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
This paper presents a study conducted in collaboration with large Canadian engineering, procurement and construction management (EPCM) firm to identify areas of improvement in the current process of progress reporting and forecasting project status at different targeted future dates. The study focused mainly on trending and time/Cost control of engineering, procurement and construction (EPC) projects. It encompassed a field study of the practices of the industrial collaborator, study of related materials from the literature, and development of standalone computer applications, which serves as add-on utilities to the propriety project management software of the industrial partner. The paper presents a model for improving trending and forecasting of time and cost in construction operations. The proposed model has 3 main functions: 1) trending of estimate accuracy, 2) integrated control and forecasting, and 3) progress visualization. @Risk 5.0 for excel, Windows SharePoint Server and visual basic for application (VBA) are used to develop 3 add-on tools to implement the developments made in the above 3 functions. Numerical examples based on a set of data from a pilot training project, developed by the industrial partner, are presented to illustrate the essential features of the developed model.
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