Detecting Information Bottlenecks in Architecture Engineering Construction Projects for Integrative Project Management
Bibliographic record
Abstract
Information bottlenecks are a central issue in architecture, engineering, and construction (AEC) projects. It is crucial to detect their occurrence to mitigate their impacts. Existing approaches to detect information bottlenecks use metrics that require estimating the quantity of information published, which is a challenging task in practice. In this paper, building on the literature and recent developments in automation, we introduce a revised model that uses unresolved activities during project delivery and frequency of information publication actions to detect information bottlenecks. We validated our model by applying it to a case study. We compared bottlenecks detected by our model to ground truth obtained in the case study independently using observational data and external verification. We further study how project activities requiring different levels of interactions among project participants with different roles (e.g., designer, contractor) affect our model’s performance. Results support our model and show that it is possible to attain comparable performance by considering only high-interdependency activities. Our research enables teams and project managers to keep a focus on issues that require intense communication among different experts and organizations to avoid bottleneck. Our contribution to the body of knowledge includes a revised model that detects information bottlenecks during project delivery and insights into integrative project management where the focus is on both project activities and collaboration needs across roles to streamline the whole process.
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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 | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".