BIM-based draft schedule generation in reinforced concrete-framed buildings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Project schedules have a vital role in the effective management of time, cost, scope and resources in construction projects, and creating schedules requires schedulers with construction knowledge and experience. The increase in the complexity of building projects and the emergence of building information modeling (BIM) in the architecture, engineering and construction industry have encouraged researchers to explore BIM capabilities for automated schedule generation. The scope and capabilities of the developed systems, however, are limited and the link between design and scheduling is still underdeveloped. This paper aims to investigate methods to develop a BIM-based framework to automatically generate schedules for concrete-framed buildings. Design/methodology/approach This system first extracts the required data from the building information model, including elements’ dimensions, quantities, spatial information, materials and other related attributes. It then applies construction rules, prior knowledge and production rate data to create project work-packages, calculate their durations and determine their relationships. Finally, it organizes these results into a schedule using project management software. Findings This system provides an automated and easy-to-use approach to generate schedules for concrete-framed buildings that are modeled in a BIM platform. It provides two schedules for each project, both a sequential and an overlapped solution, which the schedulers can modify into a practical schedule based on conditions and available resources. Originality/value This research project presents an innovative approach to use BIM-based attributes of structural elements to develop list of work-packages and estimate their durations, and then it uses a combination of rule-based and case-based reasoning to generate the schedules.
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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 | 0.001 | 0.002 |
| 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 it