The designing of 3D-printed modular artificial reefs through design thinking framework: a case study in Koh Khai, Chumphon Province, Thailand
Bibliographic record
Abstract
Coral reefs degradation in Thailand demands scalable, community-accessible restoration solutions. This study addresses the limitations of conventional artificial reefs by developing 3D-printed modular artificial reefs (3DMARs) optimized for ecological performance, usability, and low-resource deployment. Formulated through the lens of design expertise and applying a design thinking framework, the research integrates qualitative content analysis, interdisciplinary collaboration, and user-centered design to establish key criteria, including modularity, flexibility, and environmental sustainability. Prototypes were co-developed with SCG Co., Ltd. Field deployment at Koh Khai, Chumphon Province, demonstrated ease of transport, manual installation, and ecological compatibility. Initial observations suggest that the system enhances coral habitat complexity while promoting local engagement. The study presents a replicable and adaptable model for decentralized reef restoration, supporting sustainable marine efforts in regions with limited technical capacity, such as Thailand and similar Southeast Asian coastal areas.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".