Integrated framework of computer vision and fuzzy systems for lean workspace management in construction
Bibliographic record
Abstract
Efficient workspace management is essential in construction due to its direct impact on safety, productivity, and workflow continuity. However, current practices lack standardized evaluation metrics and a structured approach to workspace control. This paper introduces an integrated framework for Lean Construction workspace management using computer vision (CV) and a fuzzy inference system (FIS) to develop an automated decision support system (DSS). The CV model tracks workers and calculates five new spatial performance metrics related to space utilization, labor density, workspace occupancy, foot traffic, and storage efficiency. These metrics feed into the FIS to generate a Spatio-Temporal Index (STI), which quantifies workspace efficiency. The framework aligns with Lean Construction principles, particularly those emphasized by the Last Planner® System (LPS). An interactive Power BI dashboard visualizes trends and provides recommended strategies to assist in informed decision-making. A real-world case study demonstrates its potential for improving spatial efficiency. • Framework integrating computer vision and fuzzy system for lean workspace control. • Five spatial metrics quantify utilization, density, traffic, occupancy, storage. • Metrics translated via fuzzy system into Spatio-Temporal Index for efficiency tracking. • Metrics and Index embedded in Power BI dashboard for lean-aligned decision support. • Framework validated through renovation case study demonstrating applicability.
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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) | 0.002 | 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".