Accelerating Structural Topology Optimization for Subtractive Building Materials Reuse
Bibliographic record
Abstract
In recent years, an increasing number of recovered construction materials have appeared in the North American market. However, how and whether to make full use of those recovered building materials again in an existing building is still a difficult question. Therefore, this paper proposes a processing pipeline for the potential structural optimization of recovered building materials that may be reused. The pipeline supports the evaluation and analysis of recovered structural components in advance in order to save materials and labor in subsequent additive or subtractive fabrication procedures. The application of the processing pipeline is examined for feasibility based on a case study of structural design optimization using the example of a recovered cantilever beam. Conceptually, matching a dimensionally acceptable component that has a specific volumetric degradation pattern with a candidate-optimized material removal (subtractive) solution might qualify that component as a candidate for reuse. Alternatively, in an additive 3D printing process using recycled materials, minimization of materials use is desirable. In both cases, quickly generated matches are desirable. By reducing the mass of materials used for a structural purpose, removed material parts can be recycled for other engineering projects, and embodied carbon can be reduced significantly. Ultimately, this upgrades a circular economy in the built environment, leading to more sustainability. While this is an exploratory and highly conceptual study, the processing pipeline described is a first step toward the vision identified above.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".