Environmental Impact and Cost Assessment for Reusing Waste during End-of-Life Activities on Building Projects
Bibliographic record
Abstract
The construction industry contributes significantly to global environmental loads with massive amounts of construction and demolition waste (CDW) ending up in landfills. To address the need for efficient CDW management, this research proposes a new decision support framework for managing construction waste generated during end-of-life activities for building projects. The framework monetizes potential environmental savings from different recovery options (e.g., reuse, recycle, and so on) and uses multiobjective optimization to determine the optimal quantity of material to undergo each material recovery scenario. The framework uses parametric weights to consider stakeholders’ preferences and their appreciation of environmental benefits compared with costs. A case study of a renovation project in Waterloo, Ontario, Canada, is used to demonstrate how the proposed framework can divert concrete and glass waste from the landfill. For this particular project, savings of 200 GJ of embodied energy, 22 m3 of water, and over 12 t of greenhouse gases can be realized from optimal recovery planning using the proposed framework. This study concludes that decision support systems should be used well in advance of end-of-life activities to evaluate trade-offs for recovery planning activities effectively.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".