Equation to predict maximum pipe stress incorporating internal and external loadings on buried pipes
Bibliographic record
Abstract
Pipelines used for water and other services are very important lifelines in modern society. Commonly, these buried pipes are subjected to significant stresses due to external (traffic and earth) and internal (water pressure) loads. As many of these pipelines were laid sometime in the last century or earlier, in most cases their condition has deteriorated primarily by electrochemical and (or) microbiological corrosion. Corrosion activity (internal and external) can manifest in various forms, but in many cases will lead to reduced pipe thickness, which in turn leads to an increase in pipe stresses induced by the external and internal loads. Currently available analytical procedures to estimate pipe stresses are based on oversimplifications such as the two-dimensional (2-D) analysis based on Winkler springs, limiting their application to general pipe burial conditions. This paper describes the application of a three-dimensional (3-D) finite element method to analyse a buried pipe subjected to external and internal loads. Firstly, the finite element model is validated against the data from field tests conducted on the basis of a cast iron pipe that was laid in 1930 at Strathfield, Sydney, Australia. The results of these 3-D finite element analyses are then used to develop a closed-form expression to predict maximum stresses in pipes of different sizes buried in different soil types. Having obtained a good agreement between the proposed model outcomes and the 3-D finite element analysis results, the proposed model has been validated against the field test data under different internal and external loadings. The verified outcomes of the model reveal that it can be used to predict maximum stresses without conducting a full-scale finite element analysis, which often requires specific computational resources and computational skills. Furthermore, the proposed model can be used in probabilistic analyses, where a large number of calculations need to be carried out to account for the uncertainty of the input variables. The applications of the model are also discussed in relation to the assessment of pipe performance and remaining safe life.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".