Effect of infill architecture on structural performance and sustainability of 3D-printed reinforced concrete columns
File(s)
Accepted version
Author(s)
Nguyen-Van, Vuong
Jie, Cheah Chun
Zhang, YX
Wong, Hong S
Type
Journal Article
Abstract
This study presents a holistic design-to-performance investigation of 3D-printed reinforced concrete (RC) columns featuring sinusoidal infill patterns, targeting both structural optimisation and sustainability. The primary contribution lies in the development and fabrication of functionally graded, lightweight RC columns using additive manufacturing, integrated with conventional reinforcement and cast mortar. Among four parametrically designed configurations (CS0 – non-peak sinusoidal infill, CS4 – 4-peak sinusoidal infill, CS8 – 8-peak sinusoidal infill, CS12 – 12-peak sinusoidal infill), the CS12 column is selected for physical fabrication and testing under uniaxial compression. The experimental results provide insights into load-bearing capacity and failure mechanisms, which are then used to calibrate a validated finite element model. The model enables an extensive parametric study comparing the four infill designs, focusing on structural efficiency, stress distribution, and damage progression. A cradle-to-gate life cycle assessment (LCA), conducted following the parametric analysis, quantifies the environmental impact of each configuration per unit mechanical strength. Results reveal that columns with CS8 and CS12 infill geometries achieve superior capacity-to-mass ratios and reduce global warming potential (GWP), demonstrating that infill architecture can be strategically tuned to optimise both performance and sustainability. This work advances digital concrete fabrication by providing a scalable framework for performance-driven and environmentally responsive column design.
Date Issued
2026-03-15
Date Acceptance
2025-12-23
Citation
Engineering structures, 2026, 351
ISSN
0141-0296
Publisher
Elsevier
Journal / Book Title
Engineering structures
Volume
351
Copyright Statement
Copyright © 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Published
Article Number
122057
Date Publish Online
2025-12-29