Experimental study on water penetration and thermal resistance of large-scale 3D-printed cementitious walls
Bibliographic record
Abstract
3D concrete printing is an innovative technology in construction, offering a faster and cost-effective way to build houses and other structures. However, challenges such as the increased presence of voids in interlayer joints and higher porosity in the printed material may negatively affect the performance of 3D-printed building envelopes. Hence, in the first phase of this study, the performance of large-scale 3D-printed cementitious walls was evaluated under wind-driven rain conditions. Wall specimens with cold joints, which were built and cured during the construction of an actual 3D-printed concrete building, were tested to evaluate the real-world performance of such building envelopes against wind-driven rain. In the second phase, the thermal resistance of printed walls with different printing patterns and insulation configurations was examined. The study found that although 3D-printed concrete walls are highly vulnerable to driving rain, the inclusion of an air cavity between two wythes effectively prevents rainwater from infiltrating into the building. Additionally, uninsulated 3D-printed walls with regular hollow sections and zig-zag patterns demonstrated higher thermal resistance compared to walls made of traditional concrete masonry units (CMU). The study also found that a printed concrete wall with regular hollow section (core) with insulation in the hollow core is able meet the requirement of thermal resistance specified by Canadian building code. • 3D-printed walls with cold joints were evaluated under wind-driven rain conditions • 3D-printed walls are susceptible to driving rainwater penetration • Air cavity in a 3D-printed wall prevents driving rain from entering the building • Thermal resistance factor of 3D-printed walls is experimentally evaluated • R-value of insulated 3D-printed walls can be determined using a prediction model
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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) | 0.003 | 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".