A data-driven and knowledge-based decision support system for optimized construction planning and control
Bibliographic record
Abstract
Despite the use of various construction planning and control systems, no prior data-driven and knowledge-based system provides optimized solutions based on specific project team needs and applications. This paper presents a data-driven and knowledge-based decision support system that utilizes a knowledge database constructed from experts' experience and proposes multi-level and integrated systems for planning and control of construction projects. A mixed-method approach gathers data from industry professionals, develops a knowledge repository based on Rough Set Theory (RST), launches an inference engine using the Pyke package, and integrates these insights into a decision support system optimized by a multi-objective mathematical model. The developed system considers the functional requirements of the project team and suggests an optimized and fit-for-purpose planning and control system. To demonstrate its practicality, it applies to a real-world renovation project . This paper contributes to enhancing systematic and data-driven decision-making for planning and control systems based on expert knowledge and the specific needs of the project team.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".