Automatic Estimation System of Building Frames Integrated with Structural Design Information (AutoES)
Bibliographic record
Abstract
The project participants, including clients, architects, structural engineers and contractors would want to know accurate construction costs as soon as the design is completed. However, it may take at least several weeks for the cost estimation after the structural design and drawings are completed, depending on the project scale or size. Quantity surveying in manual is time-consuming and taken by lots of effort. Even if computerized software is used, it takes a lot of time to insert structural design information to the software. In addition, the estimated cost may result in inaccurate quantity owing to the drawing errors or quantity surveyors’ mistake, as well as it is not an exact quantity for actual construction, exposing numerous problems at the construction phase. For instance, to accurately estimate the quantity of rebar, some additional effort is required such as preparing the bar bending schedule. Such problems occur by the communication gap and viewpoint difference among project participants who perform structural designs, draft the structural drawings and estimate quantity. But, if structural design information can be automatically received for quantity estimation, an exact quantity can be estimated without omission or errors. To solve those problems, this study proposes automatic estimation System of building frames integrated with structural design information (AutoES). Using the algorithms provided by AutoES, the task of cost estimation can be accomplished with an exact bill of quantity including a bar bending schedule without errors, mistakes, or omission within a week, which used to take at least 4 weeks.
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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 | 0.002 |
| 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".