Progressive development of cracks in biochar–cement composites through multiscale analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The intrinsic brittleness of the cement matrix limits its synergy with steel reinforcement bars, constraining energy dissipation and crack control capacity of concrete. Enhancing the ductility of cementitious materials is, therefore, essential for improving structural resilience. A porous carbon material, for example, biochar, offers a sustainable alternative that can improve ductility and energy dissipation capacity, while simultaneously contributing to carbon sequestration. Despite promising experimental observation, the fracture mechanisms underlying this toughening effect remain insufficiently understood. This study addresses this knowledge gap by developing a multi-scale voxel-based modeling framework for biochar–cement composites, linking microscale mechanical heterogeneity to macroscale fracture behavior. The elastic modulus of biochar–cement paste was first quantified across nanoscale (∼nm and ∼µm) to mesoscale (∼mm and ∼cm) through nano- and micro-indentation, providing scale-bridged inputs for the model. The framework explicitly resolves aggregates, interfacial transition zones, and biochar particles within a concurrent multi-scale domain, enabling simulation of localized fracture while retaining computational efficiency. The simulation results were validated through a three-point bending test and digital image correlation. These findings demonstrated that biochar could alter the crack propagation by redistributing interfacial stress and promoting multi-layered crack deflection, which significantly enhanced the energy dissipation by up to 90%. This study elucidates the multi-scale mechanisms by which the pore architecture of biochar enhances ductility, providing a scalable framework for the design of high-ductile, sustainable cementitious materials.
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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 | 0.001 |
| 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 it