RISMET Project - Benchmark Study of Risk-Informed In-Service Inspection Methodologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper describes the main results of the RISMET benchmark project, whose final report was recently published by the OCDE/NEA. The RISMET project was started in 2005 by the Joint Research Centre (JRC) of the European Commission (EC) together with the Nuclear Energy Agency (NEA), with the goal of benchmarking several risk-informed in-service inspection (RI-ISI) methodologies. More than twenty organizations from Europe, U.S., Canada and Japan participated, representing utilities, regulators and research organizations. In the project several RI-ISI methodologies were applied to the same case, consisting of four selected piping systems in Unit 4 of the Ringhals nuclear power plant (PWR) in Sweden. The different RI-ISI methodologies were compared with each other and to the deterministic ASME XI ISI selection procedure. The scope of the benchmark was limited to four systems, but the variety regarding safety class, potential degradation mechanisms and pipe break consequences ensured a good coverage of issues for a comparative study. The risk-informed methodologies showed some significant differences and resulted in slightly different risk ranking and selection of inspection sites. However, the results of the benchmark indicated that the risk impact of these differences is small, and the RI-ISI approaches identify safety important piping segments that are ignored by approaches not using probabilistic safety assessment (PSA). The results of the benchmark exercise RISMET improve the knowledge on differences in approaches and their impact on plant safety and promote the use of risk-informed ISI.
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.
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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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 it