Mechanical and Durability Performance of 3D-Printed Concrete with Coarse Aggregates and Cold Joints
Bibliographic record
Abstract
Three-dimensional (3D) construction printing technology has advanced significantly in recent years. Its application in building construction depends on a thorough understanding of the performance of printed materials. Hence, there is a growing need for precise mechanical and durability properties in printed concrete. Additionally, since this technology involves layer-by-layer printing, understanding how the printed materials perform at interlayer interfaces is crucial. Previous research primarily examined the mechanical properties of printed cement mortar with fine aggregates. There were limited studies on printed concrete with coarse aggregates and the behavior of cold joints. We investigated the mechanical and durability properties of 3D-printed concrete featuring coarse aggregates up to 10 mm in size through experimental tests. The compressive, flexural, and shear strength of the printed concrete was examined in three directions focusing on three treatments for cold joints. We also used nondestructive ultrasonic pulse velocity (UPV) to evaluate the quality of the printed concrete. The test results revealed that incorporating coarse aggregate improved bonding shear strength in cold joints. Additionally, using bonding agents at cold joints resulted an increase in overall compressive and flexural strength by approximately 10% when compared with cold joints treated with water alone. The experimental findings also showed that the 3D-printed concrete had an 11% increase in void ratio compared with the mold cast concrete.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".