Bibliographic record
Abstract
Construction in the heavy industrial sector of Alberta makes up a large portion of the provincial economy. Unfortunately, this construction work is completed in an environment of some of the highest cost growth in the world. Projects constructed in this industry typically go over budget by 11% and on some projects, costs have even exceeded budgets by up to double. Given this situation, the industry and province are eager to reduce these cost overruns to bring greater profits and to encourage investment in the province. While research thus far has determined many potential causes and indicators of this cost growth, quantification of these factors is lacking. To truly reduce cost growth, it is necessary to determine the actual impact of many potential methods of controlling costs and also gain a better understanding of the predictive nature of various indicators. Large amounts of data was collected on 139 projects based in Alberta over the past 15 years. This data was statistically analyzed to find factors that contribute to cost growth and to develop phase-based predictive models to forecast which projects are at risk of running over budget. It was found that larger, longer and more complex projects are more likely to have cost growth. However, better engineering, increased modularization, and larger contingencies can help reduce this cost growth. Further, the amount of rework, growth in the amount of work completed in the winter and growth in the size of the peak work force are all early indicators that can help determine when a project may be facing unanticipated problems. These factors are investigated using bivariate and multiple regression procedures which identify the magnitude of the impact of each variable, allow for the prediction of cost growth and, when combined, allow the unique contribution of each variable to be determined. This detailed information allows companies to perform cost-benefit calculations to better prioritize investments into cost control measures. By controlling costs better, companies can increase their profits and the province can benefit from increased investment in an efficiently running industry.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".