Improvisation in Construction Planning: An Agent-Based Simulation Approach
Bibliographic record
Abstract
Improvisation is the decision-making process addressing unexpected obstacles in a spontaneous but rational manner. Although undesirable, as it indicates deviation from plans, improvisation is unavoidable in construction to address issues related to unforeseen uncertainties. An adaptive planning system employing improvisation to react rapidly to unplanned events may therefore boost the performance in construction projects. Accordingly, this research aims to predict the outcomes of construction planning processes from an improvisational perspective by better understanding the dynamics of improvisation. It seeks to identify how different variations of improvisational parameters influence the improvisational outcome. This objective is achieved through an agent-based model used to simulate the improvisation practices at the level of planners interacting together. Parameters relating to planners, projects, and problems influencing each planner’s improvisational means are illustrated in the model. The model’s inputs were validated through data from large-sized projects. Linear regression models that predict the results of the improvisational practices were then developed through simulation experiments. Findings regarding the impacts of different types of improvisors on the improvisational outcomes are presented. The contribution of this study lies in enhancing the overall improvisational performance in construction planning to ultimately guide decision makers and planners to better handle uncertainties in projects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".