Assessing the Seismic Performance of Underground Infrastructures to Near-Field Earthquakes
Bibliographic record
Abstract
Assessing the seismic response in road and rail tunnels is crucial for ensuring their structural integrity and safety as vital infrastructures.However, these underground spaces receive far less scrutiny compared to their above-ground counterparts such as bridges and viaducts.Moreover, comprehensive case studies with fully dynamic monitoring systems, especially in active seismic zones, are uncommon.The dynamic behaviour of man-made tunnels varies significantly based on factors like design and geological conditions, particularly the surrounding soil or rock characteristics.Tunnels excavated in shallow depths within soft soils are generally considered more susceptible to seismic forces compared to those bored through dense soil or hard rock.However, direct comparisons based on experimental data are limited in the current scientific literature.To this aim, the seismic responses of one underwater rail tunnel and two nearby mountain road tunnels to the same near-field seismic event are examined.Specifically, data from the Mw=4.4Berkeley Earthquake on the Bay Area Rapid Transit's Transbay Tube and Caldecott tunnel system's Bore 3 and 4 are analysed.This represents a rather unique case for near-fault strong motions, which are the ones expected to be most dangerous for civil structures and infrastructures.Moreover, this set of target infrastructures includes different boundary conditions (soft soil and hard mountain rock), cross-sectional shapes, year of construction, and other characteristics.This enables a detailed investigation of these contributing factors.Finally, Arias Intensity (AI) and significant duration ( 595 ) are proposed as potential explanations for the different seismic susceptibility of these different tunnels.
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 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".