Dynamic behaviour of biochar-amended soil under cyclic undrained conditions
Bibliographic record
Abstract
Railway infrastructure earthwork is subjected to traffic-induced cyclic loads, which can be particularly challenging when constructed on weak or problematic soils. To enhance the engineering properties of such soils, various soil improvement techniques, including the use of cement and lime, have been widely applied. However, these conventional methods raise environmental concerns due to their contribution to greenhouse gas emissions. As a result, there is a growing need to explore eco-friendly ground improvement alternatives. This study investigates the use of biochar as a sustainable soil reinforcement material. The shear behaviour of both unreinforced and biochar-amended soil was assessed through direct shear tests and triaxial (static and cyclic) tests. Direct shear test results showed that adding 5% biochar notably improved the soil's cohesion and internal friction angle, optimising the balance between particle interlocking and porosity. However, increasing the biochar content to 10% led to a 32.4% reduction in the internal friction angle. Static triaxial tests demonstrated that biochar reinforcement improved deviator stress and accelerated pore water dissipation compared to unreinforced soil. Meanwhile, cyclic triaxial tests revealed that higher biochar content resulted in increased accumulated axial strain and excess pore water pressure. Additionally, scanning electron microscopy analyses examined the interaction between soil and biochar particles. The findings highlight biochar's potential as a sustainable, eco-friendly soil reinforcement for enhancing ground stability in railway constructions and other geotechnical applications.
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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 | 0.001 |
| 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) | 0.001 | 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".