Response of Buried Pipelines Under Permanent Ground Movements: Physics-Informed Deep Neural Network Approach
Bibliographic record
Abstract
Abstract Long-distance underground pipelines are inexorably constructed over harsh geological fields exposing them to geohazards resulting in both transient and permanent ground movement. The permanent ground movements caused by landslides, liquefaction, ground subsidence, slope failures, fault movement, etc., can result in large deformation threatening the safety and integrity of the pipelines. Therefore, evaluation of the structural response, i.e., displacement and strain fields of the buried pipelines exposed to the permanent ground displacement is a major concern for the industry. The mechanical and physical behavior of the pipelines subjected to ground movement can be described mathematically using nonlinear Partial Differential Equations (PDEs) by adopting the Euler-Bernoulli beam theory with large deformation. The model takes into account the nonlinearities caused by the pipe-soil interaction and pipe geometry. Various methods, including the Finite Element Method (FEM) and Finite Difference Method (FDM), can be used to solve the nonlinear PDEs and thus predict the structural response. However, the FEM usually requires complicated simulation using costly commercial software. Also, both the FE and FD methods are mesh-dependent. On this basis, this research endeavor proposes and utilizes an inexpensive, novel, easy-to-implemented, simulation-free, and meshless method to get the response. As a result, Physics-Informed Neural Networks (PINNs), deep-learning neural networks that consider the underlying law of physics in PDEs, can be a potential strategy to deal with PDEs with high complexity. To this end, the neural networks thoroughly learn based on the incorporated physical laws together with the boundary and/or initial conditions, eliminating the need for large training datasets. The obtained structural response, applicability, and accuracy of the predicted results of the proposed method are assessed by comparison with the obtained results from FEM.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".