Developing a design framework for 3D-printed modular living walls: a systematic review
Bibliographic record
Abstract
Several initiatives have introduced traditional green walls as a long-term approach to climate change mitigation. The Industry 4.0 advancements have encouraged the innovative integration of digital technology in design processes. Limited research has investigated the integration of digital fabrication methods, like 3D printing, in modular living wall applications. Through a mixed-methods approach, this study aimed to identify the role and develop a design framework using digital fabrication in modular living wall. A systematic literature review of 39 articles from 2016 to 2025 was conducted by screening Scopus and Web of Science databases using the PRISMA technique. VOSviewer co-occurrence keyword mapping identified four core themes for the study’s content analysis, including design elements, design optimisation, fabrication, and performance evaluation. The findings revealed the widespread use of digital software in design modelling and post-design analysis compared to the limited use of application-based optimisation. A design framework was developed for fabricating an efficient modular living wall through a circular design process involving component identification, conceptualisation, optimisation, and evaluation stages. The structured framework provides a sustainable and efficient pathway for the 3D printing of modular vessels while supporting SDG Goal 11, sustainable cities, and Goal 13, climate action, and contributing to the fields of urban, architectural, and landscape 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 | 0.032 | 0.069 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.039 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".