Numerical investigation of bonding in stone-clad Façades: comparative analysis with and without mechanical anchorage
Bibliographic record
Abstract
Abstract Reliable simulation of bond behavior between stone façade panels and concrete substrates is crucial for safe façade design, particularly with mechanical anchorage. Conventional finite element models relying on tie constraints overestimate interface strength, especially in the absence of surface preparation or bonding agents. This study develops and validates a physically motivated, element deletion–based finite element methodology to accurately simulate crack initiation, propagation, and failure at the mortar–stone interface. The three-dimensional numerical models, implemented in ABAQUS and benchmarked against laboratory splitting shear tests, represent the composite system comprising a concrete substrate, sand-cement adhesive mortar, and a Travertine stone façade. Both unanchored and Z-type mechanically anchored configurations were examined. Results demonstrate the approach yields accurate predictions of failure loads and damage evolution: for unanchored specimens, the maximum numerical–experimental deviation was below 2%, while Z-type clip anchorage significantly enhanced the load-bearing capacity and altered the fracture mechanism. Compared to conventional tie or interface-layer models, the element deletion strategy provides a computationally efficient and transparent tool for capturing the failure behavior of stone–mortar–concrete composites. The findings offer insights for optimizing façade anchorage design and provide a validated numerical framework for future research.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".