A decision support system for evaluating construction project recovery plans
Bibliographic record
Abstract
Effective recovery planning is crucial for construction projects, known for their unpredictability and complexity. Current models for managing such projects often lack flexibility and real-time adaptability, leading to insufficient responsiveness to emerging challenges and disruptions. This paper presents a novel simulation-based Decision Support System (DSS) that integrates fuzzy logic and agent-based modeling (ABM) within an effects-based framework to evaluate recovery strategies. This DSS addresses the limitations of traditional project management tools by enabling project managers to simulate various strategies against multiple scenarios, thus aiding in making informed decisions. By blending the predictive power of fuzzy logic with the dynamic capabilities of ABM, the system provides a thorough analysis of project interdependencies and the impact of different strategies. Tested and validated on a $100 million real-world industrial project employing 280 personnel, the DSS has proven effective in improving resource allocation, minimizing project entropy, and enhancing predictability. This study underscores the value of combining expert knowledge with sophisticated simulation tools, offering managers a powerful resource for navigating project complexities and improving strategic planning, thus aiding project management and completion success.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".