Multi-objective Optimization Analysis for Selective Disassembly Planning of Buildings
Bibliographic record
Abstract
Multi-objective Optimization Analysis for Selective Disassembly Planning of Buildings Benjamin Sanchez, Christopher Rausch, Carl Haas and Rebecca Saari Pages 128-135 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Adaptive reuse has the potential to maximize the residual utility and value of existing assets through green design methods such as selective disassembly planning. Studies in the field of selective disassembly for adaptive reuse of buildings are scarce and there is no evidence of established methodologies and/or analysis for the optimization of the environmental and financial benefits. In this paper we provide a framework for the multi-objective analysis to obtain several effective selective disassembly plans through the combination of different deconstruction methods. The analysis is delineated in terms of the physical, environmental, and economic constraints of the deconstruction methods per building component. Then, a weighted multi-objective optimization analysis is incorporated to generate the set of noninferior solutions that minimizes environmental impacts and building cost. For adaptive reuse of buildings, the methods described in this study can be used to improve the project outcomes according to specific goals and constraints (e.g. environmental, economic, technical). Keywords: multi-objective optimization, selective disassembly, adaptive reuse, Circular Economy, green design. DOI: https://doi.org/10.22260/ISARC2019/0018 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".