Adaptive Automation Strategies for Robotic Prefabrication of Parametrized Mass Timber Building Components
Bibliographic record
Abstract
Adaptive Automation Strategies for Robotic Prefabrication of Parametrized Mass Timber Building Components Oliver David Krieg and Oliver Lang Pages 521-528 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: This paper presents applied research into automated and adaptive robotic prefabrication strategies for a generative platform design enabling mass customized, mass timber modular construction. The development is part of an ongoing effort by the company to bring a holistic approach of design-driven modular mass timber housing and advanced prefabrication techniques into the market of urban densification. The presented work is currently developed for the delivery of two mass timber housing projects with four and 12 storeys, the latter acting as a case study in this paper. In the first part, the paper explains the possibilities and challenges of large-scale robotic fabrication in timber construction as well as strategies for embedding robotics within a digital design workflow. The focus will be on the required change in the industry's design thinking for automation strategies to be effective. In the second part the development of an adaptable construction system suitable for robotic automation will be presented. We argue that while automation of conventional assembly steps might be suitable in some cases, the construction system, and ultimately the individual building parts, must be developed in reciprocity with the capabilities, or the design space, of the machine. The authors share their experience of the application of such an integrated process and its requirements towards the collaboration between, and the automation of, design, construction, engineering, and manufacturing. In its conclusion, the authors argue that a long overdue paradigm change in the architecture and building industry can only be achieved through the complete convergence of all disciplines. Keywords: Robotics; Prefabrication; Timber construction; Mass timber; Automation; Modular building; Computational design; Digital fabrication; Affordability; Platforms for life; DOI: https://doi.org/10.22260/ISARC2019/0070 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".