Guided wave and genetic algorithm-based inversion for characterization of pile foundations
Bibliographic record
Abstract
The characterization of geometric and mechanical properties is of great importance for the reuse of unknown foundations. In this paper, we propose a pile characterization method to non-invasively estimate the length and mechanical properties of a pile simultaneously and automatically. The forward model is established based on the guided wave theory for a cylindrical pile and a genetic algorithm-based method is proposed for the inversion process. The guided wave model is built using the spectral element method, which can generate the dispersion relation for the given physical properties. The resonance data (including the resonance frequency and the resonance number) and pile length can be related to the phase velocity , which can be derived by assuming either the displacement control or stress control boundary conditions. The loss function in the inversion method is defined by linking the dispersion relation of the forward model and the resonance analysis for those two boundary conditions. The proposed method is validated against the experimental data. By using the proposed method, pile characterization can be achieved automatically. The average accuracy of the proposed method for characterizing the pile properties (such as pile length, shear wave velocity, and longitudinal wave velocity) and the pile length is within 5 % error for each variable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".