Shake-Table Testing of a Brick Masonry Groin Vault: Overview of Blind Predictions and Postdictions and Comparison with Experimental Results
Bibliographic record
Abstract
This paper presents the results of the blind test competition carried out within the scope of the European project SERA.ta “Seismic Response of Masonry Cross Vaults: Shaking table tests and numerical validations”. The purpose of the competition was to predict the results of a set of tests carried out on an unstrengthened full-scale cross vault with mortar joints and solid bricks (then strengthened with textile reinforced mortar) subjected to a horizontal dynamic excitation. The paper offers an overview of the modelling approaches utilised, along with their corresponding predictions and post dictions. The findings are assessed based on both the damage mechanisms and predicted values for displacements and accelerations in both directions. The results are then compared with the experimental findings. Modelling approaches utilizing the Finite Element Method (FEM) yielded the most accurate predictions regarding displacements and crack patterns. Conversely, a submission employing a Discrete Element model provided the most accurate prediction of damage mechanisms. Nonetheless, the significant discrepancies in predicted displacements and accelerations underscore the necessity for continued efforts to establish consensus on appropriate modelling assumptions for masonry vaults.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".