Progressive development of cracks in biochar–cement composites through multiscale analysis
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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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 | 0.000 |
| 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) | 0.001 | 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".