Building material reuse: An optimization framework for sourcing new and reclaimed building materials
Bibliographic record
Abstract
The process of sourcing Reclaimed Construction Materials (RCMs) is predominantly manual and hindered by the limited digital presence of RCMs. However, the advancing technological landscape provides an opportunity to create a modular framework that can assess the value proposition of New Construction Materials (NCMs) and RCMs for a project before acquiring them. A field study in the Kitchener/Waterloo region inspired the development of a framework that has three underlying systems encompassing digital tools for data collection and analysis: (1) The Real Environment uses 3D scanners and a spreadsheet software, (2) The Model Environment uses Building Information Modeling (BIM) software and a Life Cycle Assessment (LCA) tool, and (3) The Core Engine uses an optimization program. Real-world data collected from RCM stores (The Habitat for Humanity) and NCM stores (The Home Depot) are used for a realistic demonstration of the framework. By practically applying the framework to source window and door components for a modeled multi-residential building design , an integrated selection of 35% RCMs and 65% NCMs was proposed for the building design. Furthermore, sensitivity analysis is also performed for validation. The framework may disrupt the ongoing building design practices that deem material reuse problematic by enabling flexible sourcing of used and new building materials . A modular and iterative framework for facilitating reuse is thus contributed.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".