Multiobjective Optimization Model for the Life Cycle Cost-Sustainability Trade-Off Problem of Building Upgrading Using a Generic Sustainability Assessment Tool
Bibliographic record
Abstract
Because existing buildings occupy most of our built environment, there is an urgent need to upgrade them considering building sustainability criteria. Therefore, many optimization models were proposed to find the optimum upgrading solution that improves the building sustainability while minimizing its costs using traditional sustainability rating tools [e.g., Leadership in Energy and Environmental Design (LEED) (US), Building Research Establishment Environmental Assessment Methodology (BREEAM) (UK), and others]. The variations among these tools hinder their application outside their original countries, calling for global tools. Therefore, this study contributes to the knowledge by developing a novel multiobjective optimization model to solve the life cycle cost (LCC)-sustainability trade-off for building upgrading using a generic sustainability rating tool. This tool includes seven sustainability criteria and 29 subcriteria, resulting in 134 decision variables. The proposed model finds the near-optimum upgrading solutions that minimize their LCC while improving the building sustainability using the multiobjective artificial immune system algorithm. The model was applied to a real case study of a large building in Montreal, Canada. The obtained solutions covered almost all the ratings ranges from pass to outstanding and showed the trade-offs between the building sustainability and LCC. This research is a step toward adopting a global sustainability rating tool to find the optimum building upgrading solutions that can address the regional limitations of the traditional rating tools.
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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 | 0.001 | 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 | 0.000 |
| 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 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".