Interface Management Model for Mega Capital Projects
Bibliographic record
Abstract
Many construction projects are becoming more complex and large in scale due to advances in technology and operations. These projects involve many stakeholders, with different geographical locations and working cultures, collaborating with one another throughout the project life cycle. Industry leaders believe that interface management systems can be created to improve alignment between stakeholders and reduce project issues and conflicts. However, identifying interfaces and monitoring interface states are significant challenges that creates a continues struggle for owners. Interfaces are generally considered as the links between different construction elements, stakeholders and project scopes. Poor management of interfaces may result in deficiencies in the project cost, time, and quality during the project life cycle execution, or may result in failures after the project has been delivered. Therefore, having systematic interface management to effectively handle the interfaces through the project life cycle is critical to project performance. In this paper, a process based approach is proposed for interface management of mega capital projects, starting with the definition and taxonomy of interfaces. Then, the main steps for implementing an Interface Management System (IMS) are introduced: (1) interface identification, (2) documentation, (3) issuing, (4) communication, and (5) closing.
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.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".