Predicting Performance Indicators Using BIM and Simulation for a Wall Assembly Line
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
Off-site home construction allows for the construction of building components to be completed in an off-site facility. The floors, walls, and roof are constructed on separate production lines, then shipped together to site for installation. This type of home construction presents a good opportunity to utilize lean manufacturing principles allied with simulation methods to better industrialize the home building process. This paper presents a case study of a well-known panelized residential home manufacturer, where the focus is the wall assembly line. Multiple key performance indicators (KPIs) are calculated in order to forecast production for each project and key result indicators (KRIs) are used to predict the outcomes of multiple projects. The predicted performance indicators are found through a simulation model of the production line using quantity take-offs extracted from BIM models. The analysis of these performance indicators will be used to evaluate project feasibility when the project is built in an off-site construction facility.
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 | 0.000 |
| 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