Sustainability Assessment of Modern High-Rise Timber Buildings
Bibliographic record
Abstract
As woodworking and construction technologies improve, the construction of multi-storey timber buildings is gaining popularity worldwide. There is a need to look at the design of existing buildings and assess their sustainability. The aim of the present study is to assess the sustainability of modern high-rise timber buildings using multi-criteria assessment methods. The paper presents a hierarchical system of sustainability indicators and an assessment framework, developed by the authors. Based on this framework, the tallest timber buildings in different countries, i.e., Mjøstårnet in Norway, Brock Commons in Canada, Treet in Norway, Forte in Australia, Strandparken in Sweden and Stadthaus in UK, were compared across the three dimensions of sustainability (environmental, economic/technological, and social). Research has revealed that none of the buildings is leading in all dimensions of sustainability. However, each building is unique and has its own strengths. Overall multi-criteria assessment of the buildings revealed that the Brock Commons building in Canada has received the highest rank in all dimensions of sustainability. The paper contributes to the theory and practice of sustainability assessment and extends the knowledge about high-rise timber buildings. The proposed sustainability assessment framework can be used by both academics and practitioners for assessment of high-rise timber buildings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".