BIM-based data mining approach to estimating job man-hour requirements in structural steel fabrication
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
In a steel fabrication shop, jobs from different clients and projects are generally processed simultaneously in order to streamline processes, improve resource utilization, and achieve cost-effectiveness in serving multiple concurrent steel-erection sites. Reliable quantity takeoff on each job and accurate estimate of shop fabrication man-hour requirements are crucial to plan and control fabrication operations and resource allocation on the shop floor. Building information modeling (BIM), is intended to integrate multifaceted characteristics of a building facility, but finds its application in structural steel fabrication largely limited to design and drafting. This research focuses on extending BIM's usage further to the planning and control phases in steel fabrication. Using data extracted from BIM-based models, a linear regression model is developed to provide the man-hour requirement estimate for a particular job. Actual data collected from a steel fabrication company was used to train and validate the model.
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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