Bibliographic record
Abstract
The goal of this research is to investigate the fundamentals of fracturing processes in heterogeneous materials such as concrete using geophysical methods and dynamic micromechanical models. This work describes how different aspects of fracture formation in concrete can be investigated using a combination of Acoustic Emission (AE) techniques, ultrasonic wave velocity imaging, and high resolution Computed Tomography (CT). Fracture formation and evolution were studied during shear failure of large reinforced concrete beams and compressive failure of concrete samples.\nAE analysis includes studying complex spatial and temporal fracture development that precedes shear failure. Predominant microcrack mechanisms were analyzed at different stages of fracture formation. CT images were used to investigate the influence of concrete microstructure on fracture topography. Combined AE and CT damage evaluation techniques revealed different aspects of fracture development, thus expanding our understanding of AE events and their mechanisms. These images show how aggregate particles influence fracture nucleation and development. An emphasis has been placed on the role of coarse aggregates during the interlocking of fracture surfaces at transferring shear stresses.\nUltrasonic wave velocity and AE techniques have been applied to uniaxial compression tests of concrete with various aggregate sizes and strengths similar to that of the concrete beams. AE parameters, p-wave velocities, and stress-strain data have been analyzed concurrently to image damage evolution under compression. Influence of material composition on microcracking and material state changes during loading has been investigated in detail. The results of compressive tests were used as building blocks for developing realistic micromechanical numerical models of concrete.\nThe models were designed using a distinct element code, where material is modelled through the combination of bonded particles. A number of procedures were developed to transfer the exact microstructure of material incorporating its visual representation into the model. The models’ behaviour has been verified against experimental data. It was shown that these models exhibit realistic micromechanical behaviour. The results of the experimental investigation of concrete fracturing were expanded by modelling more cases with aggregate size and strength variations.\nIt was shown that geophysical imaging techniques, along with advanced micromechanical numerical modelling, can help us understand damage formation and evolution.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".