Algorithmic mix design for 3D printing materials

Sergis, Vasileios et Ouellet-Plamondon, Claudiane. 2024. « Algorithmic mix design for 3D printing materials ». In Proceedings of the Canadian Society of Civil Engineering Annual Conference (Whistler, BC, Canada, May 25-28, 2022) Coll. « Lecture Notes in Civil Engineering », vol. 359. pp. 915-922. Springer.

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Résumé

Additive manufacturing technology aims to revolutionize the construction sector. Researchers are looking for the optimum materials to use in mix design to control the fresh and final properties of the mix.Those properties are contradictory to each other and finding the optimal mix design has always beena challenge. Developing an optimization tool that considers trade-offs among a variety of competingobjectives can improve the mix design process. In this study, the mortars contained combinations of multiplefactors, including the cement type, sand type, water content, and admixtures. Three properties investigatedare flowability, buildability, and compressive strength. The buildability was assessed by measuring theshear stress with the direct shear apparatus based on the ASTM D3080. The workability was acquired bymeasuring the flow spread of the mortar mixes following the ASTM C1437, and the compressive strengthfollowing the ASTM C109. A multiobjective Pareto-optimization method is used to improve the propertiessimultaneously. Feedforward neural networks were used to predict the properties of new mixes. The geneticalgorithm was used to optimize the network parameters. This approach yields promising capability toimprove the competing objectives of the mortar mixes by considerably reducing the time and the numberof experiments.

Type de document:	Compte rendu de conférence
Professeur:	Professeur Ouellet-Plamondon, Claudiane
Affiliation:	Génie de la construction
Date de dépôt:	21 févr. 2024 15:08
Dernière modification:	20 janv. 2025 18:31
URI:	https://espace2.etsmtl.ca/id/eprint/28369

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