Application of the Hybrid Finite/Discrete Element Method to the Numerical Simulation of Masonry Structures
Bibliographic record
Abstract
Masonry is one of the most common construction techniques around the world. Unfortunately, it is also among the most sensitive to the risk of failure due to exceptional types of loading, such as, but not limited to, earthquakes. In order to properly design new structures and assess the strength of the existing building stock, it is important for the engineering community to have a reliable means of performing numerical simulations of masonry structures.\nMasonry is characterized by being a composite material, which translates into greatly inhomogeneous and anisotropic behaviour. Typical numerical tools do not account explicitly for these features of the material, and those that do often require a high level of information about the structure in order to properly calibrate some of their many parameters.\nThis research introduces a new numerical tool, the Finite/Discrete Element Method (FDEM), as a possible solution to this problem. In this method the discontinuities between different materials are explicitly accounted for in the simulation. The structure and the materials are explicitly modelled with little to no need for numerical calibration. \nFDEM, implemented in the software Y-Brick, has been used to replicate the results of experiments on masonry structures of different level of complexity. Some of the experiments were performed at the University of Toronto as part of this research; other have been collected from the technical literature. The software has been shown to be able to reproduce the experimental results from both the quantitative and the qualitative points of view, suggesting that it could become a valid tool for the simulation of the behaviour of complex masonry and structures under the effects of various types of loadings.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".