A Framework for Construction Strategy Formulation and Visualization
Bibliographic record
Abstract
Informed decisions on the efficacy of project and construction strategies to meet client objectives and satisfy project delivery constraints involve the three tasks of formulation, representation and assessment of alternative strategies. This need is present in not only the initial planning phase of a project, but during the execution phase as necessitated by changed objectives and conditions. Explored in this paper is a way of thinking about the concept of construction strategy and its interrelated parts (tactical variables and plan) in aid of the foregoing tasks, with particular emphasis on large scale vertical and horizontal building and civil infrastructure projects. This way of thinking has been pursued as a complement to other work directed at the visual representation and assessment of construction strategy alternatives in the form of 4D images, construction schedules, and related images, such as the distribution of resources in terms of time, space and physical system. Motivating this work is the pressing need to reduce impediments associated with exploring alternative strategies in a timely and insightful manner using existing scheduling and 4D modeling tools. Our strategy framework takes into account the dynamic factors of a project and consequences for strategy formulation and adaptation as positioned in the domains of time, space/system and project participant, and as driven by project directives.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".