Development of an Online Calculation Tool for Safety Evaluation of Pipes Subjected to Ground Movements
Bibliographic record
Abstract
Abstract Underground pipelines are inevitably installed in unstable geohazard areas associated with the possible development of significant ground deformations. Under ground movement, excessive strains can be generated in the pipe wall, which poses a threat to pipeline integrity. This study aims to develop an industry-oriented calculation tool for safety evaluation of pipes subjected to ground movements induced by a variety of nature and construction-related hazards. The tool, comprised of deterministic and reliability-based analyses, is designed within MecSimCalc which is an innovative online platform for creating and sharing web-based Apps for individuals and groups. Calculation flow behind the tool is developed according to a novel method proposed based upon the finite difference method (FDM). Given grid nodes along the pipe, a large set of simultaneous finite-difference equations are constructed based on nonlinear governing differential equations of the Euler-Bernoulli beam under large deflections. The nonlinearities arising from pipe material, pipe-soil interaction, and geometry of the pipe are considered within the model. As unknowns of the finite-difference equations, the axial and lateral displacement of the pipe at each grid node can be obtained using nonlinear equation solvers. This method is utilized to predict the strain demand in the limit state function for reliability-based assessment. Applying stochastic properties for each basic parameter, the probability of failure can be calculated using Monte Carlo Simulation. Meanwhile, the program is compiled using Numba in Python and then optimized by the parallelization technique to enhance computational efficiency.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".