A Model for Implementing & Continuously Improving the Automated Change Management Process for Construction Mega Projects
Bibliographic record
Abstract
In the construction industry, project changes constitute a major cause of delay, disruption, cost increases, poor quality and unsatisfying performance.They are also known as a main factor of litigation between the owners and constructors.Construction projects are essentially process-based, and with the critical role of Information Technology (IT) in this industry, projects, especially mega projects, are being managed remotely.The volume of data and documents, such as Requests For Information (RFIs), different types of Change Requests (CRs) and Project Change Notices (PCNs), being transferred and exchanged amongst the stakeholders of a project is considerable.The circulation of these documents amongst project stakeholders still heavily relies on two methods or levels for management of change process; Conventional (fully paper-based, Faxes, Snail mails) and Electronic or semi-automated (Email, Internet, PDF files).These two methods are dependent on human discipline to follow specified processes which often break down because of human nature.This creates serious compliance problems.It is hypothesized here that fully that automated process-based management of change can lead to better compliance, more efficient management of change, and reduction of time and cost of data exchange.An approach of doing this is presented in this paper.Ultimately, its impact should improve project performance.Conclusions are presented on time savings and improved compliance.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".