Pragmatic seismic collapse meso‐scale analysis of old Dutch masonry churches
Bibliographic record
Abstract
Abstract The demand for advanced nonlinear time‐history and seismic collapse simulations of old unreinforced masonry (URM) constructions to support informed risk evaluation and mitigation plans is rapidly increasing in the structural engineering profession. On one hand, offering cutting‐edge solutions based on the latest advances is challenging for practitioners, given the reduced timeframe usually available for projects and the specialized knowledge required. On the other hand, researchers frequently face difficulties in accessing old buildings and gathering key data required in complex numerical collapse analysis strategies. In this work, a pragmatic approach for evaluation of the earthquake collapse response of vulnerable old URM churches typical of the Northern Netherlands, now exposed to low‐magnitude induced seismicity due to gas extraction, is presented. To bridge the gap between academic and industry applications, an integrated framework is proposed that combines archival and onsite research, code‐based prescriptions, geometrical characterization and simplified discrete element modeling. Main outcomes include the identification of recurrent damage patterns for five old URM churches erected during the 11th, 13th, 14th, and 19th centuries, representative of key traditional multi‐leaf and cavity‐wall structural types, as well as relevant failure mechanisms and collapsed debris distributions for seismic signals of varying intensities. Produced results constitute a solid foundation of data on which to base the design of ad‐hoc retrofits and development of tailored risk assessment models. This study opens a new line of inquiry while discussing practical challenges and research questions which arose, to be of interest to both applied researchers and structural engineering professionals.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".