Experimental characterisation of hydraulic lime mortar and clay brick elasticity using the Virtual Fields Method
Bibliographic record
Abstract
Digital image correlation (DIC) offers the ability to calculate strain fields on the surfaces of test samples. This data can be used to characterise the mechanical properties of construction materials during compression tests. In this paper, a practical inverse technique called Virtual Fields Method (VFM) is customised for the characterisation of elastic properties (Young’s modulus and Poisson’s ratio) of construction materials during compression tests. More specifically, hydraulic lime mortar and solid clay brick materials, which are commonly found in both historic and modern structures, are examined. When applied to slender rectangular prisms away from contacts, VFM and extensometer-based characterisation demonstrate excellent agreement, with average deviation less than 5%. However, VFM characterises elasticity more comprehensively and robustly than standard extensometer-based methods. To account for through-thickness strain variations due to contact force eccentricities, VFM identification results from opposing faces of the sample are averaged. Furthermore, local surface defects are identified with the equilibrium gap indicator and excluded via virtual field constraints to achieve unbiased characterisations. The accuracy of the characterisation is evaluated by reconstructing the applied forces on sample cross sections and comparing these to the measured forces. This workflow is applied when VFM is used to characterise elasticity during splitting tests on flattened brick cores. Here, VFM yields results that deviate on average by only ∼ 10% from analytical estimates, even though it does not rely on predefined analytical expressions. This example illustrates the ability of VFM to identify material properties under complex stress states, opening up new possibilities for improved characterisation of construction materials. This can be particularly useful for identifying the properties of materials from non-standard samples extracted from historic buildings.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".