ERIES: Advancing frontier knowledge in earthquake, geotechnical and wind engineering through experimental research
Bibliographic record
Abstract
This contribution offers an overview of European collaborative efforts toward increased understanding and risk-focused mitigation strategies through the EU-funded ERIES (European Research Infrastructures for European Synergies, www.eries.eu) project. ERIES provides transnational access to advanced experimental facilities across Europe and Canada, fostering knowledge development in structural, seismic, wind, and geotechnical engineering. The paper outlines the project’s organisational framework, primary research goals, and thematic areas of focus.Illustrative case studies currently underway at the EUCENTRE Foundation in Pavia, Italy, are shown to demonstrate the scope of research supported by ERIES. These examples showcase how foundational research enabled by this funding initiative can significantly enhance understanding of seismic damage in structures. The project addresses critical issues such as mainshock-aftershock sequences in seismic risk analysis and the refinement of experimental loading protocols. Additionally, in-situ dynamic testing of base-isolated structures offers a unique chance to assess these mitigation devices’ operational performance, furthering innovative experimental approaches.In essence, ERIES is a key platform for fostering research collaboration across Europe, particularly in structural, seismic, wind, and geotechnical engineering in addition to the wealth of experimental data that will be produced as a result. Through its framework and transnational access opportunities, ERIES enables impactful research that improves the understanding of structural damage and informs risk assessment practices, with broad societal benefits.
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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 | 0.023 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".