Discontinuum models for the structural and seismic assessment of unreinforced masonry structures: a critical appraisal
Bibliographic record
Abstract
In the last few decades, discontinuum (or discrete, discontinuous) numerical modelling strategies – i.e. those capable of representing the motion of multiple, intersecting discontinuities explicitly – have become increasingly popular for the structural and seismic assessment of unreinforced masonry (URM) structures. The automatic recognition of new contact points and prediction of large deformations up to complete separation are unique features of discontinuum-based models, making them particularly suitable for unit-by-unit simulations. The adaptation of discrete computational models, primarily used for analyzing rock mechanics and geomechanics problems, to the conservation, structural and earthquake engineering evaluation of URM assemblies is still ongoing, and recent advances in computer-aided technologies are accelerating significantly their adoption. Researchers have now developed fracture energy-based contact models tailored to unreinforced masonry mechanics, explored discontinuum analysis from the mortar joint- to the 3D building-level, combined discrete modelling strategies with analytical or continuum approaches, integrated the latest structural health monitoring and image-based developments into discontinuum-based analysis framework. Concurrently, new and still unsolved issues have also arisen, including the selection of appropriate damping schemes, degree of idealization and discretization strategies, identification of appropriate lab or onsite tests to infer meaningful equivalent mechanical input parameters. This paper offers to the research and industry communities an updated critical appraisal and practical guidelines on the use of discontinuum-based structural and seismic assessment strategies for URM structures, providing opportunities to uncover future key research paths. First, masonry mechanics and discontinuum-based idealization options are discussed by considering micro-, meso- and macro-scale modelling strategies. Pragmatic suggestions are provided to select appropriate input parameters essential to model masonry composite and its constituents at different scales. Then, discontinuum approaches are classified based on their formulation, focusing on the Distinct Element Method (DEM), Applied Element Method (AEM) and Non-Smooth Contact Dynamics (NSCD), and an overview of primary differences, capabilities, pros and cons are thoroughly discussed. Finally, previous discontinuum-based analyses of URM small-scale specimens, isolated planar or curved components, assemblies or complex structures are critically reviewed and compared in terms of adopted strategies and relevant outcomes. This paper presents to new and experienced analysts an in-depth summary of what modern discontinuum-based tools can provide to the structural and earthquake engineering fields, practical guidelines on implementing robust and meaningful modelling strategies at various scales, and potential future research directions.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".