Identifying factors affecting waste production throughout the construction project life cycle and proposing BIM-based solutions
Bibliographic record
Abstract
Purpose Because the construction industry is one of the largest waste producers, understanding the primary reasons for waste production is essential. The goal of this study is to identify the major causes of waste production over the project life cycle in Iran's construction industry and to propose effective solutions based on modern technologies like BIM. Design/methodology/approach After identifying the primary causes of construction and demolition waste production through interviews and literature analysis, solutions based on building information modeling (BIM) were provided. Then, using questionnaires and exploratory factor analysis (EFA), the areas impacting waste reduction were found. Findings The findings suggest that “prefabrication” is the best approach for improving time and quality, while “detection and prediction of errors in the design and construction phases” is the most cost-effective technique for addressing cost and environmental issues. Originality/value Cost, time, quality and environmental concerns may all be influenced by effective waste management throughout the project life cycle. Furthermore, utilizing state-of-the-art technologies has far-reaching implications for reducing material waste, resulting in more environmental-friendly construction.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".