Advanced digital solutions for construction waste management: A 4D BIM integrated scenario analysis
Bibliographic record
Abstract
Construction generates substantial material waste with environmental and economic impacts, yet current methods rarely integrate waste estimation with project schedules. This study develops and validates semi-automated 4D BIM framework that links element-level quantities to standardized resource specifications — CSI MasterFormat and the Iranian Cost Estimation Standard (ICES) — and propagates material-specific waste factors into a time-phased (Autodesk Navisworks Timeliner) analysis. Two custom applications (“Material-DB” and “Waste-Estimation”) import ICES items, map them to MasterFormat codes, compute waste by unit (area/volume/mass), and populate BIM parameters that are visualized over the construction programme. A municipal office project in Tehran ( ≈ 6800 m 2 , four floors) is used for validation. BIM-based estimates align with observed site waste within ≤ 17% deviation across key streams (e.g., concrete −7.6%, masonry −14.0%, tiles +8.0%, iron −16.7%), with concrete waste peaking early during foundations and primary envelopes. Five design/material scenarios demonstrate how the method supports fast scenario testing and cost appraisal; for example, Scenario 3 yields the lowest waste cost, whereas Scenarios 5 and 2 are highest. The framework enables schedule-aware waste planning, targeted design changes (e.g., exterior wall systems, stone/tile use), and routine waste audits, offering a replicable path towards time-cost-waste trade-off decisions in early project phases.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".