Geographically Distributed Hybrid Testing of a Multi-Storey Timber Structure on Compliant Soil: Pilot Design
Bibliographic record
Abstract
Abstract Timber construction is rapidly advancing in structural engineering due to its benefits, such as light weight, ease of prefabrication, and contribution to societal goals. With an increase in building height, however, knowledge garnered by coupling advance modelling and experimental testing will enable designers to push the boundary further. The ERIES-HYSTERESIS project will use geographically distributed hybrid testing to investigate energy dissipation characteristics and soil-structure interaction (SSI) responses of multi-story buildings constructed with mass timber. Testing large-scale timber structures with SSI considerations poses unique challenges, requiring an innovative hybrid testing methodology. Geographically distributed hybrid simulation involves designing a representative pilot structure and dividing it into subcomponents for simultaneous testing. This paper details the 3D design of the pilot structure, considering constraints required for hybrid testing. SSI is addressed as a critical factor, with the objective of identifying the hierarchy of failure between the soil, the wall-foundation connection and the hold-downs. A balance has been achieved between maintaining a realistic design and ensuring the experimental requirements remain within the capabilities of the involved laboratories.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".