Fuzzy decision-driven multi-dimensional seismic fragility analysis of shield tunnels in liquefiable strata
Bibliographic record
Abstract
Accurate seismic fragility analysis is essential for ensuring the post-earthquake safety of shield tunnels in liquefiable strata. Current fragility methodologies predominantly adopt empirically selected ground motion intensity measures (IMs) and single damage measures (DMs), while overlooking uncertainties stemming from sampling insufficiency and inhomogeneity. This study presents an advanced fragility analysis framework to systematically address these limitations. Initially, a computational model of a shield tunnel in liquefiable strata is developed and validated through experimental and theoretical simulations. Subsequently, fuzzy probabilistic seismic demand models are established considering uncertainties in model parameters based on nonlinear dynamic analysis results. Progressing to the fuzzy decision-making phase, an approach integrating fuzzy analytic hierarchy process and fuzzy technique for order preference by similarity to ideal solution is implemented to quantitatively determine the optimal IM. Finally, multi-dimensional fragility analysis is conducted, using the DMs reflecting tunnel deformation and uplift. The results reveal that sustained maximum velocity, as the optimal IM, produces more conservative estimates of tunnel damage probability than the conventional IM, peak ground acceleration. Moreover, the implementation of multi-dimensional fragility analysis incorporating dual DMs yields significantly improved reliability in seismic risk assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".