NHERI Lehigh Experimental Facility With Large-Scale Multi-Directional Hybrid Simulation Testing Capabilities
Bibliographic record
Abstract
The NHERI Lehigh Experimental Facility, as part of the NSF funded Natural Hazards Engineering Research Infrastructure (NHERI) site, was established in 2016 as an open-access facility. This facility enables researchers to conduct state-of-art research on natural hazard mitigation in civil infrastructure systems, including high performance numerical and physical testing to improve the resilience and sustainability of the civil infrastructure against natural hazards. The facility has the unique ability to conduct real-time multi-directional hybrid simulation (RTHS) on large-scale structural systems using 3D nonlinear numerical models combined with large-scale physical models of structural and non-structural components. The Lehigh Experimental Facility processes testbeds that include a testbed for testing lateral load resisting system characterization testbed, a non-structural component multi-directional dynamic loading simulator, full-scale and reduced-scale damper testbeds, a tsunami and storm surge debris impact force testbed, and a soil-foundation structure interaction testbed. This paper describes the infrastructure and capabilities of the NHERI Lehigh Experimental Facility. Developments by the facility in advancing large-scale RTHS are given. Examples of research projects performed by users of the facility are then provided, including large-scale RTHS of steel frame buildings with magneto-rheological (MR) dampers and nonlinear viscous dampers subject to strong earthquake ground motions; 3D multi-hazard large-scale RTHS of tall steel buildings subject to multi-directional wind and earthquake ground motions; characterization of a novel semi-active friction device based on band brake technology; and testing of cross-laminated timber self-centering coupled wall-floor diaphragm-gravity systems involving multi-directional loading.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".