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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".