A Research Roadmap for Off-Site Construction: Automation and Robotics
Bibliographic record
Abstract
The development of a research roadmap was undertaken to further the activities of a joint industry-university-government initiative in off-site construction research in Canada. The roadmap identifies the general research areas of structural design, construction materials, building science, advanced manufacturing, logistics and transportation, automation and robotics, and digitized construction. The development of the roadmap included a broad literature review of peer reviewed academic journals, select conference proceedings, and industry publications. The review of recent research in these areas was analyzed from the perspectives of application area, technology area and innovation phase. The purpose of the analysis was to identify the current activities and opportunities for further research. For example, in the area of automation and robotics, the results showed the majority of construction automation research relates to the actual production phase, as opposed to planning or operations. In terms of innovation maturity, little research is being undertaken with respect to the implementation and adoption of automation technologies, and very little research in technology development or prototyping. In addition, applied research is being conducted at approximately half the rate of basic research. A more recent trend has been greater research interest in industrial production technologies, particularly in additive manufacturing. Very little research is being conducted with respect to non-robotic cyber-physical systems including, IoT connectivity, drone technologies, or construction focused actuator and manipulator technologies. This paper will discuss the broader results of the research roadmap with a focus on automation and robotics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.032 | 0.017 |
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".