ERIES: ADVANCING FRONTIER KNOWLEDGE IN EARTHQUAKE ENGINEERING THROUGH LABORATORY TESTING
Bibliographic record
Abstract
This paper provides an overview of European research synergies towards loss and risk-driven mitigation approaches, focusing on the EU-funded ERIES (European Research Infrastructures for European Synergies, www.eries.eu) project. ERIES aims to provide transnational access to advanced experimental facilities in Europe and Canada, supporting the advancement of knowledge in structural, seismic, wind, and geotechnical engineering. The project’s structure is described, along with its broader research goals and themes addressed. A case-study example project currently underway at the Eucentre Foundation in Pavia, Italy is highlighted to illustrate the kind of research possibilities available within ERIES. It shows how the fundamental research that is possible using this funding mechanism can have notable impacts on the characterisation of damage accumulation in structures, shedding much-needed light on issues like mainshock-aftershock sequences in seismic risk assessment but also the calibration of experimental loading protocols used in laboratory testing. To sum up, the ERIES project is a driving force for research collaboration in Europe, specifically in the areas of structural, seismic, wind, and geotechnical engineering. Its framework and transnational access projects allow for impactful research to be conducted, leading to better understanding of damage in structures and informing risk assessment methods, benefiting society as a whole.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".