Digital twin for production estimation, scheduling and real-time monitoring in offsite 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
The variability in production operations in offsite construction factories undermines the effectiveness of using average production rates for estimating production time and scheduling. In fact, production schedules based on average rates often exhibit significant deviations from actual production. This study proposes a digital twin for production estimation, scheduling, and real-time monitoring in offsite construction. By integrating computer vision, ultrasonic sensors, machine learning-based prediction models, and 3D simulation, the digital twin continuously collects time data from the shop floor, estimates cycle times, simulates operations, generates production schedules, virtually mirrors operations in real time, and enables the generation of updated schedules based on actual progress. In a case application to a wall framing workstation, the production schedule generated using the digital twin for the framing of wall panels during a work shift achieves an 81% reduction in deviation from actual production time compared to the conventional fixed-rate method commonly used in current practice.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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