Understanding and managing iterative error and change cycles in construction
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
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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