Visualization Configuration Model for Integrating Presentation of Construction Project Management Data
Bibliographic record
Abstract
Construction management tasks involve and produce voluminous, multidimensional data. Although many tasks currently are supported with software tools, it still requires great mental effort for project personnel to read information in datasets and analyze relationships from one dataset to another. Information visualization is widely considered to hold the potential of providing insights from datasets by visually presenting project management data and information relationships. Many visualization solutions are available to the construction domain, but they usually focus on specific application tasks, lacking flexibility and data integration in user-interaction for construction management. This paper proposes a Visualization Configuration Model (VCM), a novel visualization technique for construction project management. Integrated with the Industry Foundation Classes (IFC) data model, the VCM is developed to be a configurable visualization environment, facilitating user-interactions to explore voluminous information. The model framework and underlying theories for integrating model components are provided. Through analysis of two case scenarios, the paper demonstrates the capability of the VCM supporting view configuration and illustrates the potential of visualization to benefit construction management tasks.
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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 | 0.003 | 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 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".