Effect of 3D Printing Parameters on Hollow Vascular Networks for Self-Healing Concrete Using Recycled Materials
Bibliographic record
Abstract
Self-healing concrete can repair and seal cracks, this study explores fused deposition modelling (FDM) for creating novel vascular networks and tubes using polylactic acid (PLA) as a material they are incorporated within the concrete beam to injection the healing agent.The problem addressed in the text is to understand how interaction different printing parameters on different layer thicknesses (0.10, 0.20, 0.30, 0.40, and 0.50) mm affect the mechanical properties of (PLA) samples produced through (FDM) with a 3D printer, which was investigated using standardized tests.The hardness and tensile strength were determined using the ASTM D2240 method and ASTM D638-10, while water absorption was assessed using the ISO 62 standard.The bending properties of the specimens were analyzed using the ASTM D790-10 three-point bending test.Tensile and flexural strength increased up to 72 MPa and 81 MPa respectively as layer thickness increased up to 0.4 this layer was chosen to print the hollow vascular network.
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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 | 0.000 |
| Scholarly communication | 0.001 | 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".