Decision-making for UBC High Performance Buildings: Multi-criteria Analysis for Integrated Life Cycle Models
Bibliographic record
Abstract
The current paradigm of building design is evolving rapidly and building developers are beginning to dopt sustainable building practices across Canada. Attaining a sustainable built environment is challenged by the complexity of decision-making and stakeholders need to examine a large number of sustainability metrics to support a 'good decision'. Each sustainable building development has a design path unique to the values of the building stakeholders.This project outlines a framework that assists decision-makers in achieving a building design that is closely aligned to their values and requirements. \n \nThis paper outlines a decision-support system that brings together a broad set of sustainability metrcs, both quantitative and qualitative, into a multi-criteria decision analysis tool where decision-makers can contrast and compare the simulated performance of competing building dsigns. The performance modeling tools include environmental life cycle analysis (Athena EIE), financial modeling by life cycle costing (UBC ID), energy modeling (eQuest). Benchmark information, required for informing decision-makers of baseline conditions, is derived from the UBC_LCA database, UBCPT, and UBC Operations data. Social benchmarks are determined from the UBC Post occupancy protocol under development at UBC. These metrics and benchmarks are synthesized and integrated into the multi-critera decision analysis framework as optional attributes from which decision- makers can select as decision criteria. \n \n \n \nset of sustainability indicators are developed from metrics specified by ISO 21912-1, LBL, ASHRAE andUBCs own criteria developed as part of the UBC Buchanan and CIRS projects. Finally, the paper discusses how decision-makers can express their preference for each critria so that their expertise and values are accurately reflected when analyzing the criteria performance results. Methods to check for 'future-proofing' are also discussed in terms of checking the life cycle models for resilience to future change.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".