Framework for enhanced progress tracking and control of linear projects
Bibliographic record
Abstract
Purpose – This paper aims at improving progress tracking and control of repetitive projects by developing a novel framework that automates the documentation of as-built information directly into the project schedule and also introduces enhanced linear scheduling formulation to support project control decisions. Design/methodology/approach – The proposed framework uses e-mail technology to facilitate detailed tracking of daily as-built events of all parties through bidirectional communication between site and head office. It also provides a new formulation for more accurate critical path and linear scheduling computation to accurately update the project's time and cost during construction. Findings – Using a case study of a road project, the paper proves that the proposed framework reduces as-built documentation effort and its schedule updates are more responsive to all as-built events than traditional scheduling techniques. Research limitations/implications – The proposed method applies to linear projects (e.g. highways) and can be extended to other repetitive projects such as high-rise buildings. It can also be extended to include voice features and procedures for forensic schedule analysis. Practical implications – The developed methodology presents a low-cost approach to document timely progress information for decision makers of massive linear projects (often associated with infrastructure) to have better control over the execution of projects, save documentation time and cost, and avoid disputes and problems. Originality/value – This research contributes in improving construction productivity by collecting timely as-built information using affordable communication technologies. It also presents novel advancements to the existing scheduling and control techniques to suit linear projects, which are most challenging.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".