A proposed conceptual framework for Computational Design Sustainability in Industrialized Building
Bibliographic record
Abstract
The construction industry has benefited from the recent methodological advancements in Computational Design (CD) and its associated technological developments. However, the multifaceted challenges faced by the construction industry have limited its capacity to achieve global sustainability goals. In this context, Industrialized Building (IB) has opened new avenues to take advantage of technology while promoting the incorporation of sustainability principles to mainstream construction problems. Despite its great potential, the literature in this area is fragmented, and the relationship between various aspects of these topics is not fully understood. This paper aims to bring awareness to the potential integration of IB and CD in promoting sustainability, thereby advancing the understanding of the topic by proposing this integration for future investigations and unravelling their relationships and underpinning ideas. The critical discussion presented in this paper proposes a common ground on which to build new knowledge in seeking to disclose conceptual patterns and links instead of specific causal mechanisms. We propose that this integration paves the way for creating a trade-off structure to manage these multifaceted and complex factors through "satisficing" – finding the satisfactory solutions rather than the optimized ones – the design, operation and delivery of a building project (and its construction value chain) in a more sustainable way.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".