Identifying factors affecting waste production throughout the construction project life cycle and proposing BIM-based solutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it