Seismic Resilience of Carbon Fiber Reinforced Polymer Renewed Riveted Steel Pipe Using Finite Element Modeling
Bibliographic record
Abstract
Carbon Fiber Reinforced Polymer (CFRP) was designed per AWWA C305-18 Standard to renew sections of 90, 70, and 52-in. diameter riveted steel penstock pipes at a power plant in a seismically active area of California. The primary purpose of the CFRP lining was in 2018 not only to increase the structural capacity of the riveted steel pipe but also increased durability, strength, and corrosion resistance for the pipe. For the design of CFRP renewal, AWWA C305-18 Standard focuses on primary structural loads applied on the pipe but does not consider seismic resiliency In the present study, the seismic resiliency of CFRP liner renewed 90-in. riveted steel pipes was modeled using finite element modeling (FEM). Seismic performance of pipe samples was simulated using permanent ground deformation (PGD) obtained using American Lifeline Alliance 2005 (ALA) guidelines. The seismic resilience of the pipeline depends on the loading direction. In this study, the ground deformation (movement) was considered normal to the pipe axis. The three modes of failure of the renewed pipe for the load resistance factor design (LRFD): (1) tension rupture, (2) local buckling due to compression, and (3) general buckling were investigated and are discussed. This work shows that CFRP lining designed per AWWA C305-18 Standard increased the seismic resilience of a damaged or even an undamaged steel pipe, in addition to the other benefits identified above.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".