AI-assisted 3D model generation for discontinuum-based analysis of URM buildings
Bibliographic record
Abstract
This research presents a novel framework for the discontinuum-based analysis of unreinforced masonry (URM) buildings, integrating artificial intelligence (AI) assisted object detection and segmentation into the structural analysis workflow. Recent advancements in machine learning, particularly Convolutional Neural Networks (CNNs), are utilized to digitize a URM building, and the most relevant construction quality parameters (e.g., block size and staggering ratio) are automatically captured from the vision-based data. The collected information is used to inform the implemented block generation algorithm, which places masonry units into wall sections that are not documented (or poorly detected) due to various on-site obstructions. Then, the digital replica of the building is turned into an evidence-based computational model using the discrete element method (DEM), where detected masonry units are represented as discrete rigid blocks in a fully discontinuous setting. The mechanical interaction between rigid blocks is predicted using a cohesive frictional contact model to capture the unit-mortar interface (bond) behavior. The AI-assisted DEM-based model is later used to perform nonlinear pushover analysis to predict the seismic behavior and collapse mechanism of the analyzed building. Hence, it is demonstrated that the proposed approach offers a great potential for discontinuum-based analysis of URM buildings by eliminating the time-consuming model generation process and providing the most representative construction quality features in the structural analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".