Investigation of bevel-ended large-span soil-steel structures
Bibliographic record
Abstract
Soil-steel structures (SSS) are increasingly used as a solution for roadway and waterway overpasses. The inlet and outlet of the buried steel structure may encounter different end treatments and bevelling slopes to accommodate different design aspects. Current design codes require precise numerical analysis of SSS considering soil-structure interaction to evaluate the stability of the steel structure and unbalanced surrounding backfill. The current study evaluates the performance of large-span SSS considering different end treatment conditions. The numerical modeling approach was validated by conducting three-dimensional nonlinear finite element model for the world’s largest-span soil-steel structure and comparing the model predictions with field observations. The steel structure has a span of 32.40 m and was constructed using a corrugation profile of 237 mm depth and 500 mm pitch. The ends of the structure were strengthened by attaching 400 mm X 1090 mm circumferential reinforced concrete collars. The numerical analysis predicted the same deflection trends as the field measurements at different construction stages. Furthermore, the validated numerical approach was employed to evaluate the performance of bevel-ended SSS. The benefits of stiffening SSS by using circumferential concrete collars and steel mesh reinforcements buried in the surrounding soil are evaluated in terms of the stability of steel structure and surrounding soil. The results indicated significant longitudinal bending moment induced in the steel plates that were affected by the slope of the beveled ends. It was also found that the steel mesh reinforcement and concrete collars reduced the induced straining actions in the steel structure by 30–50%. The obtained results demonstrate the necessity of rigorous numerical simulation in controlling the design and cost of SSS.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".