ASSESSMENT OF LOCAL ENVIRONMENTAL IMPACTS IN CONSTRUCTION PROJECTS USING A KPI-BASED APPROACH
Bibliographic record
Abstract
The construction sector is a major global source of environmental pollution, with significant direct and indirect impacts on the environment.Environmental aspects are divided into nine categories, one of which is local issues.Construction causes a multitude of local issues that serve as significant sources of environmental, social, and economic challenges for workers and nearby communities.The insufficient research works and systems for measuring the adverse effects of environmental concerns, both qualitatively and quantitatively, create significant challenges for effective environmental management.To address the challenges, this paper aims to propose a Key Performance Indicators (KPIs)-based approach to assess and analyze local environmental impacts in construction projects by identifying relevant indicators, integrating them into 4D workflows, and enabling data-driven decision-making for sustainable practices.By integrating environmental KPIs with 4D Building Information Modeling (BIM), the model dynamically reflects how each task affects the identified KPIs throughout the project schedule, influencing environment, community well-being, and economic performance over the project lifecycle.The proposed approach provides a holistic understanding of local concerns and facilitates stakeholder collaboration through shared and quantifiable metrics.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".