Earthquake and deterioration inclusive probabilistic life cycle assessment (EDP-LCA) framework for buildings
Bibliographic record
Abstract
With increasing demand to reduce the carbon emission of buildings, it is crucial to quantify the life cycle environmental impact of new buildings, including the environmental impact due to natural hazards, such as earthquakes. This study presents a novel comprehensive probabilistic framework to quantify the environmental impact of buildings, including uncertainties in the material extraction and production, transportation, construction, seismic exposure and aging (including deterioration), and end-of-life stages. The developed framework is used to quantify the environmental impact of a 3-story residential building located in Vancouver, Canada. The results show that there is a significant variation in the environmental impact of the prototype building in each stage of the life cycle assessment. If the prototype building is hit by the design level earthquake, it is expected that the median environmental impact of the prototype will be further increased by 42%. In addition, by accounting for the probability of occurrence of different earthquakes within a 50-year design life of the prototype building, the earthquake related damage will result in an additional 5% of the initial carbon emission of the building. This shows the importance of including earthquake hazard and deterioration in whole building life cycle assessments.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".