AM Perspectives: Research in additive manufacturing for architecture and construction
Bibliographic record
Abstract
As technology continues to evolve, it has made a significant impact on the field of architecture and construction. The introduction of additive manufacturing, also known as 3D printing, has transformed the way architects and construction professionals approach design and engineering. With the ability to print structures layer by layer, this technology offers a level of precision and complexity that was previously impossible. The current state of research on additive manufacturing for architecture and construction is rapidly expanding, and this book aims to provide an overview of the latest developments in this exciting field at the Technical University of Darmstadt, Delft University of Technology, and the University of Minho. The book covers the materials and processes used in additive manufacturing for construction, the challenges and opportunities of scaling up 3D printing for large-scale projects, and case studies of innovative 3D-printed structures. We explore the potential implications of additive manufacturing for sustainability, affordability, and accessibility in the construction industry. Looking to the future, we offer ideas and insights into the possibilities of 3D printing as an ecosystem. As technology continues to improve and costs decrease, additive manufacturing could become a widespread and accessible method of construction, with implications for everything from disaster relief housing to space colonization. The potential for customization and personalization in 3D printing also offers exciting possibilities for individual homeowners and small-scale projects. We aim at providing a comprehensive overview of the current state of research on additive manufacturing for architecture and construction, while also exploring the exciting potential for this technology in the years to come. We hope this book will inspire architects, engineers, and researchers to explore the possibilities of 3D printing and to embrace the opportunities for innovation and creativity that it presents.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".