Multi-Level Modelling Strategies for Accurate Assessment of Masonry Arch Bridges
Bibliographic record
Abstract
This keynote lecture presents multi-level FE modelling strategies developed within the Computational Structural Mechanics group at Imperial College London for nonlinear simulation of masonry bridges. Most masonry bridges and viaducts were built more than a century ago and are still in use representing key components of roadway and railway infrastructure systems in different countries around the world. Material deterioration and increased traffic loading have led to the progressive development of damage and cracking in the brick/blockwork potentially leading to substandard performance. Accurate assessment is required to evaluate structural safety and guide the implementation of effective strengthening measures. It should be based on a realistic representation of the complex interaction among the different components including arch barrel, spandrel walls, backfill and piers in multi-span bridges. The developed 3D and 2D mesoscale and macroscale models for masonry bridges are based on different scales of representation to model material nonlinearity in masonry. Backfill materials are modelled by elasto-plastic continuum descriptions taking into account the inherent cohesive and frictional characteristics, while the physical interfaces between the different masonry parts and the backfill are represented by nonlinear interfaces allowing for separation and sliding. Detailed mesoscale models enable separate descriptions for masonry units and mortar joints providing a high-fidelity representation of the material response and the incorporation of existing damage and cracking. More efficient macroscale models guarantee a reduced computational cost. They still allow for the typical 3D response of masonry bridges but require detailed calibration of the model material parameters. Numerical examples comprising comparisons against the results from physical experiments on fullscale specimens and monitoring data on realistic bridges are presented for a critical appraisal of the developed multi-level modelling strategies for masonry arch bridges.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".