Topical collection: robotic solutions for digitally enabled production processes in construction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Across the global construction sector, a new generation of robotic systems is rapidly entering the market.Solutions for on-site drilling, spraying, masonry, logistics, and finishing are now being piloted at an unprecedented pace.Their deployment in emerging construction robotics hubs in Singapore, Hong Kong, Canada, Dubai, Abu Dhabi, Egypt, Denmark, Switzerland, and Germany demonstrates both the momentum of this technological shift and the considerable challenges that remain.In real-world testing environments, the integration of these robots into digital construction pipelines-particularly BIM-to-robot workflows, semantic task modeling, and robust digital twins-continues to be a bottleneck.These challenges position digitally enabled fabrication and robotics as a priority topic within academia, motivating research on methods, techniques, algorithms, and workflows that can accelerate adoption in construction.This Topical Collection brings together research spanning the emerging landscape of digitally enabled construction robotics.The contributions advance robotic fabrication, from flexible timber processes to innovative formwork, reinforcement, and earth-based additive methods, alongside computer vision, BIM integration, and sensing approaches that improve monitoring and quality assurance.The collection also includes mobile and aerial systems for inspection and mapping to support system autonomy in construction.Together, these works show how integrated perception, planning, and sociotechnical understanding of human-robot collaboration are becoming essential for reliable robotic performance in construction.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it