An Integrated Framework for BIM Development of Concrete Buildings Containing Both Surface Elements and Rebar
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
As-built Building Information Models (BIM) are increasingly used to facilitate the management of all aspects of built infrastructure’s life cycle. Existing studies mainly focus on automating as-built BIM development for surface elements but often ignore embedded elements such as rebar due to the inaccessibility with typical sensing devices, such as image-based or time-of-flight-based methods. To tackle the issue, this research utilizes Ground Penetrating Radar (GPR) together with the photogrammetry method to generate BIMs for in- service buildings considering both surface elements (e.g., column, slab, wall, etc.) and rebar. As the first step, as-built BIM for surface elements is generated and then existing rebar is identified by using GPR. A calibration label is designed and attached to elements which are scanned by GPR device, and a series of images are captured from those elements and then used with other images to generate point clouds. Faster RCNN is then utilized to recognize labels among all images. Next, an inverse photogrammetry approach is deployed to identify the scanned elements in BIM. By matching the recorded timestamps of GPR data and labeled images, links between the rebar in GPR data and elements in BIMs are successfully established. Finally, IFC (Industry Foundation Classes) is developed to generate as-built BIM models. Six case studies demonstrate that the system is capable of automatically developing as-built BIM, while embedded rebar could be efficiently localized and projected into corresponding elements in BIM.
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.
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 it