Integrating Life Cycle Assessment in Space Layout Planning for Minimizing the Embodied Carbon Emissions of Modular Buildings - Identification of Influencing Factors
Bibliographic record
Abstract
In modular building, space layout planning (SLP) involves the strategic arrangement and dimensioning of modules to enhance space efficiency and functional performance.Since SLP has a major influence on reducing the environmental burden when influential attributes are being decided, integrating SLP with Life Cycle Assessment (LCA) is important for enhancing the sustainability of modular buildings.However, most LCA studies have primarily emphasized the construction and operation phases as key stages for impact reduction, often neglecting the design stage.Even when the design stage is considered, modular specifications are frequently neglected.To address this gap, this study aims to extract specific modular SLP factors that significantly influence the comparative LCA of potential SLP alternatives.This paper builds on identifying key SLP parameters affecting the comparative LCA of modular buildings.By identifying influential SLP parameters on comparative LCA, we refine the methodology to accurately capture these parameters in existing layouts, thereby enhancing the precision and applicability of LCAs in modular construction.The research methodology begins by filtering SLP parameters through defined criteria to reach the main list of factors that can be affecting comparative LCA.Based on these factors, a novel method is developed to effectively extract SLP parameters from the layout.The SLP is prepared to extract the identified factors by identifying, labeling, and/or numbering modular components in this method.This research develops a structured methodology for identifying and extracting key SLP parameters, improving LCA integration, and supporting sustainable modular design.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".