BIM-Based Automatic Extraction of Daily Concrete and Formwork Requirements for Site Work Planning
Bibliographic record
Abstract
Material planning is important in construction, for it affects procurement, cost, and schedule. Proper planning of material supply and logistics helps streamline the performance of all tasks through the avoidance of excessive or insufficient material supply. Material planning relies on quantity takeoff (QTO) and project schedules. Conventionally, quantity takeoff was a manual process based on 2D drawings and human interpretation and was error-prone. Presently, with the popularity of Building Information Modelling (BIM), in BIM-based projects, using inbuilt quantity takeoff functions, quantities of work can be generated automatically from BIM models to aid the quantity takeoff. However, if those inbuilt QTO solutions are object-based, then the quantities of works extracted may not meet the requirements of the users in selected cases, e.g., in zone-based construction projects. Also, for estimating daily material requirements, the accuracy of the quantities of work becomes more important, not only for the purpose of efficient planning but also for reducing construction waste. Since works using the same type of material can go overlapping, in addition to estimating the amount of material for each work, the total amount of material for a day must also be calculated. Thus, this research aims to develop a framework for automatic extraction of zone-based concrete volumes and formwork positions for cast-in-place concrete structures using the data in BIM models, followed by linking them with project schedules for estimating daily concrete and formwork requirements. This framework extends the body of knowledge by introducing an innovative algorithm for automatically calculating overlapped areas between concrete members and a rule for naming tasks in the schedule, followed by evaluating the formwork requirements without drawing formwork in a 3D model. A software tool will be developed to achieve the aim, and a case study will be used to validate the proposed framework.
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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 | 0.000 |
| 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".