Integrating life cycle assessment in space layout planning for minimizing the embodied carbon emission of modular buildings – identification of influencing factors
Bibliographic record
Abstract
Abstract Space layout planning (SLP) is the process of identifying the dimensions and arrangement of physical spaces to enhance functionality and efficiency. It is considered a cornerstone of design optimization to control and improve a building’s lifecycle behaviour from different perspectives. It is, therefore, essential to understand the SLP factors that influence the building’s architectural design, particularly in residential buildings, because of the diversity and relationships between required spaces. The space layout optimization literature, however, has mostly focused on construction cost and operational energy usage as minimization functions, overlooking a comparison between design alternatives based on their lifecycle impacts. Despite the growing research in life cycle assessment (LCA) of building construction methods, there is a significant area of opportunity in the literature regarding specific SLP factors that influence the LCA of modular buildings. In response to this gap, the paper aims to identify the SLP parameters that significantly affect the LCA of modular buildings for potential SLP alternatives. To achieve this goal, the research methodology starts with a comprehensive literature analysis to identify relevant SLP parameters, focusing on architectural considerations. These parameters are further examined through a critical review of LCA literature to determine their impact on the lifecycle behaviour of modular buildings. The identified parameters are filtered based on their nature and influence on the LCA process. The key design factors influencing the LCA of various modular building layouts are validated through an expert survey method. By synthesizing findings from previous research, the study confirms the relevance and impact of selected parameters on the environmental performance of modular buildings. This approach allows designers to focus solely on the parameters they can genuinely influence—those factors that significantly impact the LCA of various layout options—while setting aside elements outside their control. By narrowing the focus in this manner, future research can better support sustainable design choices. Ultimately, the findings of this study assist designers and decision-makers in the early stages of modular building projects to concentrate their efforts on the most meaningful sustainability decisions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".