Simulation-Assisted Project Data Integration for Development and Analysis of As-Built Schedules
Bibliographic record
Abstract
As-built schedules are an essential tool for evaluating contractors’ schedule performance and analyzing delay and lost productivity claims. Yet, most often construction schedules are not updated frequently and/or accurately as required, which limit the availability of as-built schedules. Furthermore, the retrospective development of as-built schedules, when sufficient and reliable project data is available, is a lengthy and costly process. This study describes a simulation-assisted modeling approach that automatically processes and integrates schedules progress data and develops as-built schedules at the activity level. The proposed method uses conceptual entities that are central to the operation of simulation models and whose content changes as they route through the schedule network model. The approach introduces (1) an entity information model that records relevant schedule information either in a materialized or virtual form, and (2) an entity lifecycle model that imitates the possible routes an entity instance may maneuver through in a schedule network model which, together, simultaneously respond to schedule logic and invoking duration changes. To demonstrate its effectiveness, a prototype based on the framework was developed using Excel and MS Project and was tested with a real case study. The study is expected to facilitate the development of as-built schedules for the analysis of delay and time extension claims.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".