Navigating Complexity and Scheduling in Mega Construction Projects: Integrating FRAM with PMBOK for Enhanced Project Management — A Case Study of the Channel Tunnel Project
Bibliographic record
Abstract
The management of complexity and scheduling in mega-construction projects is a persistent challenge due to their vast scope and inherent intricacies. This study explores the application of the Functional Resonance Analysis Method (FRAM) in construction project management, focusing particularly on its utility for large-scale projects. Through an extensive literature review, a notable gap was identified in the application of FRAM throughout the entire lifecycle of mega projects. To address this, the study proposes an innovative model that integrates FRAM with the Project Management Body of Knowledge (PMBOK) process groups and enhances it using the Analytic Hierarchy Process (AHP) across all knowledge areas. This model is exemplified through a Channel Tunnel project case study, demonstrating FRAM’s ability to dissect complexities, highlight critical factors, and sequence activities effectively. Our findings illuminate the nuanced interdependencies within the Channel Tunnel project and showcase the efficacy of the FRAM model in enhancing project visibility, decision-making, and overall management. By promoting the integration of FRAM, this study contributes a novel perspective to project management, advocating for a more resilient and adaptive approach to managing the challenges of mega-construction projects.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".