Topology Optimization in 3D Concrete Printing to Reduce Greenhouse Gas Emissions
Bibliographic record
Abstract
The construction industry, responsible for 9% of global CO2 emissions and 40% of extracted natural resources, faces the challenge of reducing Greenhouse Gas (CH4, N2O, fuorinated gases, and CO2 dominant in the civil sector) emissions and managing waste sustain-ably. To address these challenges, a digital design for manufacturing methodology is proposed, which combines gradient-based topology optimization (TO) with additive manufacturing (AM) for cementitious structural design, leveraging the advantages of complex and non-traditional optimized forms. The methodology entails initially creating a finite element (FE) simulation for TO to minimize compliance within the three-dimensional design domain, taking into account volume workspace and other AM constraints. Following this, the optimized design is converted into a CAD model, and a CAM script is generated in G-Code language. Subsequently, the design is executed through a 3D Concrete Printing (3DCP) system, thereby integrating CAD-CAE-CAM technologies. The research evaluates the potential for mass reduction through TO structures and carbon dioxide emissions of 3DCP compared to traditional methods, emphasizing the potential of digital fabrication for eco-efficient construction. The observed margin highlights promising opportunities for the optimization and implementation of sustainable practices in the field of civil engineering and construction.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".