Digital Integration in Construction: A Case Study on Common Data Environment Implementation for a Metro Line Project
Bibliographic record
Abstract
This study examines the deployment of a Common Data Environment (CDE) during the extension of a major North American metro line—an infrastructure project marked by complex stakeholder dynamics and fragmented digital practices. Employing a four-phase action research approach (diagnosis, planning, implementation, evaluation), the research identifies inefficiencies in existing document management through contract reviews, field observations, and stakeholder interviews. In response, three standardized processes were introduced to streamline document workflows within the Autodesk Construction Cloud (ACC). These processes enabled partial automation of data handling, reduced reliance on manual inputs, and improved the consistency of information exchanges. While constrained by limited governance and executive engagement, the initiative demonstrates the potential of CDEs to support digital integration and automation in construction. Findings highlight the need for early planning, field-level support, and a strategic framework to ensure sustainable adoption. The results contribute practical insights for leveraging CDEs to enhance automation in large-scale infrastructure 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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".