Risk-Informed Load and Resistance Factor Design (LRFD) Methods for Piping
Bibliographic record
Abstract
The main objective of structural design is to insure safety, functional, and performance requirements of a structural system for selected target reliability levels, for specified period of time and for a specified environment. As this must be accomplished under conditions of uncertainty, risk and reliability analyses are deemed necessary in the development of such methods as risk-informed load and resistance factor design for piping. This paper provides a summary of the methodology and technical basis for reliability-based, load and resistance factor design suitable for the ASME Section III, Class 2/3 piping for primary loading, i.e., pressure, deadweight and seismic. The methodology includes analytical procedures, such as the First-Order Reliability Method (FORM) for calculating the LRFD-based partial safety factors for piping. These factors were developed in this paper for demonstration purposes, and they can be used ultimately in LRFD design formats to account for the uncertainties in strength and in the load effects. The technical basis provided in the paper is suitable for a proof-of-concept in that LRFD can be used in the design of piping with consistent reliability levels. Also, the results from additional projects in this area, including future research for piping secondary loads, will form the basis for future code cases.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".