Automation of Quantity Take-off for Modular Construction
Bibliographic record
Abstract
Quantity takeoff, serving as a foundation for the downstream tasks in the construction management, is a repetitive work. However, this process in current practice involves massive manual interventions, which is extremely time consuming and highly error-prone. This is partially due to the fact that incorporating cost breakdown structure formulated according to industry companies’ classification system into BIM still remains a challenge. This study thus exploits a methodology which allows construction practitioners to obtain quantity takeoff in an automatic manner. The main concept is to pre-load the unique classification information into the BIM model such that the quantity of materials in a given BIM model can be extracted and stored into a database (Excel Sheet) automatically in according with the preloaded classification system. Besides this, the unique classification information, along with formulas for derivedquantities, is front-loaded into the Excel Sheet database. As a result, the explicitly extracted quantities are converted by the preloaded formulas to the required format for the purpose of ordering and purchasing. A prototype system is developed based on Autodesk Revit through Revit Application Programming Interface. A case study of a modularized house reveals that a considerable amount of time saving and accuracy increasing of project estimation are achieved as a result of achieving the quantity takeoff automation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".