Effect of the employed soil constitutive model on the response of large-span soil steel bridges to soil and truck loading
Bibliographic record
Abstract
Current design codes and standards recommend refined analysis methods to accurately predict the behaviour of large-span soil-metal bridges. Consequently, the FE modelling of these structures must employ an appropriate constitutive model for the soil capable of mimicking its performance under various loading conditions. This study investigates the influence of different soil constitutive models on the performance of soil-steel arch bridges with spans up to 32.4 m. A comprehensive analysis was performed using a validated 3D finite element model to evaluate the suitability of three widely adopted soil models: the Mohr-Coulomb (MC) model, the Hardening Soil (HS) model, and the Hardening Soil model with small-strain stiffness (HSs). The results indicate that the choice of soil model significantly affects the predicted structural response. The MC model consistently overestimates the settlement and lateral displacement of the foundations, particularly for larger spans, while underestimating upward deformations of the culvert crown during backfilling. Under traffic loading, the MC model underestimated the outward bending moment at the culvert crown by up to 83 % for smaller spans but showed a reduced discrepancy for larger spans. Overall, the HS and HSs models provided more accurate predictions of the vertical and lateral displacements, bending moments, and thrust forces. The findings underscore the importance of selecting an appropriate soil model in finite element analyses to ensure reliable design and safe performance of soil-steel composite structures. • Soil-structure interaction of large-span soil steel bridges. • The choice of soil model significantly affects the predicted structural response • The MC model consistently overestimates the settlement and lateral displacement of the foundations. • Overall, the HS and HSs models provided more accurate predictions of the displacements, bending moments, and thrust forces.
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 | 0.003 | 0.002 |
| 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 | 0.001 |
| 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".