Advanced digital solutions for construction waste management: A 4D BIM integrated scenario analysis
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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