Adaptive time-truncated coupled FEM–BEM method for seismic soil–tunnel interaction in alluvial basins
Bibliographic record
Abstract
Seismic analysis of tunnels embedded in sedimentary valleys demands accurate modeling of wave–soil–structure interaction (SSI) with manageable computational effort. Coupled finite element–boundary element methods (FEM–BEM) are effective for such problems, combining FEM’s strength in capturing local soil heterogeneity and BEM’s capability to efficiently model wave radiation. However, direct time-domain BEM (TDBEM) faces prohibitive computational costs due to extensive convolution histories. This study introduces a residual-based adaptive time truncation method for hybrid FEM–BEM simulations of tunnels under seismic loading. The proposed approach dynamically adjusts the memory window based on a residual error criterion, retaining recent contributions exactly and approximating older terms through an exponentially decaying tail with controlled error. Validation against classical benchmarks and application to lined tunnels in sedimentary valleys confirm that the adaptive method maintains high accuracy compared to full-history solutions while reducing runtime and memory requirements by up to 80%. This methodology thus provides a rigorous yet computationally efficient framework for practical seismic evaluation of underground infrastructure in complex geological conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".