Jin, Willy, Caron, Jean-François and Ouellet-Plamondon, Claudiane M..
2025.
« Minimizing the carbon footprint of 3D printing concrete: Leveraging parametric LCA and neural networks through multiobjective optimization ».
Cement and Concrete Composites, vol. 157.
Compte des citations dans Scopus : 7.
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Abstract
Concrete 3D printing proposes an off-site industrial process allowing to deposit material only where required. However, most mixture design methods struggle to perform, which is why a majority of 3D printing materials display high clinker contents. This study proposes a reproducible methodology for tailor-made 3D printing materials. Applied to a low-clinker quaternary blend, an iterative optimization process leads to a significant reduction of labor in material tuning. It involves life cycle assessment and artificial neural networks as objective functions in the Pareto selection of best-performing solutions. Following the constitution of an 18-mixture database with 6 independent variables and 5 objective functions, printable mortars of different strength classes are designed within 2 to 4 active learning runs. Consequently, this optimum-driven technique allows to rapidly converge toward low-carbon solutions for 3D printing, using local materials and custom characterization procedures.
| Item Type: | Peer reviewed article published in a journal |
|---|---|
| Professor: | Professor Ouellet-Plamondon, Claudiane |
| Affiliation: | Génie de la construction |
| Date Deposited: | 03 Jan 2025 21:21 |
| Last Modified: | 10 Jan 2025 18:39 |
| URI: | https://espace2.etsmtl.ca/id/eprint/30347 |
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