OpenFRESCO: Open Software Framework for Experimental Setup and Control for Distributed Test
Bibliographic record
Abstract
Supported by the recent advancement of experimental test methods, numerical simulation, and high-speed communication networks, it is possible to distribute geographically the testing of structural systems using hybrid experimental-computational simulation. One of the barriers for this advanced testing is the lack of flexible software for hybrid simulation using heterogeneous experimental equipment. To address this need, an object-oriented software framework is designed, developed, implemented, and demonstrated for distributed experimental-computational simulation of structural systems. The software computes the imposed displacements for a range of test methods and coordinates the control of local and distributed configurations of experimental equipment with OpenSees APIs. The object-oriented design of the software promotes the sharing of modules for experimental equipment, test setups, simulation models, and test methods. The communication model for distributed hybrid testing is similar to that used for parallel computing to solve structural simulation problems. As a demonstration, a distributed pseudodynamic test was conducted using a client-server approach, in which the server program controlled the test equipment in Japan and the client program performed the computational simulation in the United States. The distributed hybrid simulation showed that the software framework is flexible and reliable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".