Quantifying the Impact of Change on the Progress of Construction Projects
Bibliographic record
Abstract
Change management is an integral part of any construction project. Although changes in scope in the construction phase are mainly caused by owners, they can occasionally be caused by contractors. Previous studies and experience have shown that scope change can have a substantial impact on the project’s progress, budget, schedule, and labor performance. In fast-tracking projects, sometimes poor-quality engineering drawings are issued for construction, which necessitates changes during the project’s execution. In this research, the authors address the indirect impact of engineering changes on construction labor performance during the project’s construction phase. This method involves using baseline schedule, earned value management system (EVMS), and an actual progress tracking tool. The occurrence of changes in the engineering scope is integrated with the EVMS charts to illustrate the impact of engineering changes on the construction labor performance factor (LPF). This method provides quantitative measurements of the labor performance loss due to engineering changes. It was applied using data from a real construction project to quantify the LPF loss due to changes. The results show that the LPF deteriorates in direct proportion with the number of changes made to the original scope.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".