Experimental study on porosity-permeability regulation and strength enhancement of 3D-printed rock analogs: New insights from post-processing explorations
Bibliographic record
Abstract
Sandy 3D printing is an emerging technique enabling fabrication of artificial rock analogs with designed structures and controllable physical properties, finding widespread application in rock mechanics and geosciences. However, the low strength, stiffness, and high porosity-permeability of sandy 3D-printed rock analogs (3D-PRA) limit their utility for simulating natural rocks across broader application scenarios. This study explores three modification infiltrates to quantitatively regulate 3D-PRA properties. We systematically evaluate their effects on the physical, mechanical, and hydraulic properties of modified specimens. Permeability-porosity relationships and strength enhancement are analyzed integrally, while compression tests assess mechanical properties and failure behavior. Notably, modification with the three infiltrates increases unconfined compressive strength by over tenfold, exceeding 50 MPa. Permeability is quantitatively regulated across orders of magnitude—from Darcy-scale (∼10D) to millidarcy levels (∼0.2mD). For aqueous nano-silica solution (ANSS), porosity and permeability vary systematically with infiltration parameters. Conversely, polyacrylic resin adhesive (PARA) and epoxy resin adhesive (EPRA) infiltrates significantly reduce permeability but exhibit no quantifiable parametric relationship; porosity trends similarly lack mathematical correlation. Microstructural evolution mechanisms are elucidated through micro-computed tomography (μ-CT) and scanning electron microscope (SEM) analysis. This work contributes a comparative analysis of infiltrate effects on 3D-PRA property regulation and proposes a precision-controlled ANSS-based process for simultaneous permeability-porosity management and strength enhancement, significantly expanding application potential.
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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".