Achieving Sustainable Structural Steel Design by Estimating Fabrication Labor Cost Based on BIM Data
Bibliographic record
Abstract
Structural steel is heavily utilized in the construction industry from residential and commercial buildings to oil and gas projects. For steel fabrication companies as suppliers of steel structures, submitting competitive project bids requires substantial knowledge of the company's practices on the shop floor and extensive experience to interpret that into credible cost estimations. Being able to make reliable estimates would contribute to the company's competitiveness in the long run. In this study, the total quantity of worker-hours or man-hours required for each major subdivision of a project is considered as the variable of interest in estimating a steel fabrication project, mainly because of the labor-intensive nature of steel fabrication. In collaboration with a partner company, three years of project data, were collected by matching the company's building information modeling (BIM) system with their labor costing system resulting in over 3,000 records, each representing the quantity takeoff for 46 design features and the worker-hours expended in shop fabrication. Stepwise regression and error analysis are used to recognize the most crucial design features in estimating project worker-hours, allowing discovery of the minimized set of inputs for estimating worker-hours and characterization of the estimation uncertainties. This labor cost estimation benefits estimators and shop production planners in that they can configure labor resources to deploy, schedule shop floor production, and recognize estimates’ associated errors, based on the company's historical data. This study is an example of using BIM data and providing tools for structural engineers to consider steel fabrication and possibly achieve more sustainable designs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".