Automatic Material Estimation by Translating BIM Data into ERP Readable Data for Panelized Residential Construction
Bibliographic record
Abstract
Offsite construction methods have been promoted and recognized globally as advanced construction techniques. Panelization, a two-dimensional form of offsite construction has brought about many benefits including lower transportation requirements and design flexibilities. In North America, the panelized construction technique has become popular particularly for wood-frame wall panels for residential construction. However, although utilizing this advanced construction method can greatly improve the working environment and productivity, the conventional mentality in construction, which overlooks the value of an automated management system to support offsite prefabrication and onsite installation, hinders its potential. An automatic material estimation system can capture all computer-aided design (CAD) data from building information modeling (BIM) files automatically and convert them into enterprise resource planning (ERP) readable data so that the ERP system can be updated for the purpose of material estimation. With the assistance of a feature-based modeling method, the proposed automatic material estimation system can save huge amounts of manual work for quantity take-off and all required CAD data can be captured by the system in a few seconds. Although highly dynamic market demands result in continual changes in design, production plan, schedule, and inventory levels, the adoption of an automatic material estimation system could account for these dynamic changes in order to maximize production. Therefore, in this paper, an automatic material estimation system is proposed in order to enhance the design and estimation process in the manufacturing environment. The proposed system is introduced and the development of supportive software is described. The proposed system is implemented as a case study in a panelized construction manufacturing facility, ACQBUILT, Inc., based in Edmonton, Alberta, Canada.
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How this classification was reachedexpand
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 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".