Current State of Interface Management in Mega-construction Projects
Bibliographic record
Abstract
Taking into account the increasing complexity and scale of construction projects recently, interface management (IM) has been emerging as an important aspect of project management practices. It is believed that effective IM improves alignment and reduces conflicts among project stakeholders by increasing visibility on roles, responsibilities and deliverables, particularly in large projects. The recent improvements in the communications and information management technologies make it possible for the global mega-project, such as oil-sand, off-shore facilities, to employ IM as a part of their project management process. Mega-projects generally are defined to be more than $1 billion; however, projects with lower cost but high organizational and interface complexity are also good candidates for IM adoption. Although there is a high demand for IM, it has not been well-defined yet, which limits its full adoption. As part of a research program to identify and establish the definitions and best practices of IM, sponsored by the Construction Industry Institute (CII), the authors investigated the current state of IM in 37 projects. These projects, including owners and contractors working within different construction sectors, are surveyed according to their general characteristics (e.g., cost, project types, etc.) and IM adoption. Furthermore, the research team analyzed the IM adoption with respect to several interface risk and complexity factors within these projects. The expected contributions of the research will be a comprehensive study of the current state of IM in construction mega-projects, identifying the factors that may lead to implementing IM in projects, and providing the definitions and preliminary principles for establishing effective IM.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".