Monitoring 3D movement of structures and soil masses using fiber optic cables under the notion of finite element method
Bibliographic record
Abstract
Conventional methods and distributed fiber optic sensing (DFOS) are increasingly adopted in monitoring soil-structure interaction projects (pile foundations, retaining structures, underground stations). Despite their wide application in two-dimensional (2D) problems, a straightforward applicability to three-dimensional (3D) cases has not proved feasible. With the aim to extend the applicability of DFOS in Civil Engineering projects experiencing three-dimensional movements such as large dams, tunneling, and many other cases where soil deformations are determinant for the response of related structures, a new method is proposed in this paper. The fundamental principle of the method arises from the use of the notion of the finite element analysis (FEA), where the real space of a fiber optic carrier is subdivided into elements bijectively related to a master element. The method was implemented in a Python code and validated against a simplified two-dimensional laboratory test associated with high-resolution data (±1.0 μ ε) and noiseless strain data generated from 3D finite element analyses. The sensitivity of the method to noisy data, associated with realistic conditions and the resolution of DFOS analyzers, was further investigated, revealing the limits of the method’s applicability and the factors affecting the accuracy and reliability of the resulting 3D displacement predictions.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".