Understanding and managing iterative error and change cycles in construction
Bibliographic record
Abstract
Abstract Errors and changes in construction often result in significant schedule and cost overruns affecting project performance. To understand the nature of these errors and changes and to ultimately reduce their detrimental impacts on project performance, this paper presents a system dynamics‐based construction model, which focuses on the dynamics of error and change management in construction, including quality management, scope management, the request for information process, and the decision‐making process for the approval of changes, and their consequent detrimental impacts on project performance. In particular, the developed model integrates several concepts in traditional network‐based tools to enhance the applicability of the model. Describing the dynamic behaviors generated by the developed model and applying the model to a couple of real‐world construction projects, this paper concludes that: (1) realism should be added to schedule planning; (2) an efficient coordination process is needed; (3) proactive contingency plans need to be taken into consideration; and (4) integration of network‐based tools and system dynamics‐based models can contribute to management of errors and changes. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".