3D Model-Based Quantity Take-Off for Construction Estimates
Bibliographic record
Abstract
Reliable estimation of construction project costs requires generation of accurate quantity take-offs. Quantity take-offs are, traditionally, experience-based exercises that are often tedious and time-consuming. Although the BIM platform has been used to improve accuracy and efficiency of quantity take-offs, its widespread use is limited by the intellectual property issues associated with the transfer of complete BIM models or standardized IFC files from owners to contractors. Instead, owners may provide 3D model review files, such as Navisworks, that are capable of integrating 3D models created by various modeling platforms, to contractors. However, this platform does not explicitly provide essential 3D model data, such as component type, shape information, and geometric dimensions, that are essential for generating quantity take-offs. This significantly limits the use of the Navisworks platform, requiring estimators to manually create necessary data for millions of model items. Here, a novel computational approach that addresses these limitations, allowing quantity take-offs to be generated quickly, accurately, and cost-effectively, is proposed. The proposed method was cross-validated by comparing the obtained results to manual measurements. A practical case study based on an industrial construction project in Alberta, Canada, was conducted to test the functionality of the proposed method.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".