Results in Engineering (Mar 2024)
The impact of nozzle diameter and printing speed on geopolymer-based 3D-Printed concrete structures: Numerical modeling and experimental validation
Abstract
3D Concrete Printing (3DCP) is a promising technology with significant advantages for the construction industry, encompassing reduced labor costs, diminished carbon dioxide emissions, enhanced time efficiency, improved user convenience, and design flexibility. To ensure optimal structural outcomes and resource efficiency, integrating numerical modeling and simulation is imperative for predicting structural behavior and identifying potential premature failures in 3DCP. Despite the recognized importance of this integration, the impact of different printing parameters on numerical models, particularly with variations in printing speed and printing layer, remains an unexplored area. This research delves into the influence of crucial printing parameters, specifically printing speed and nozzle diameter, on the buildability of 3D-printed structures within the built environment. A comprehensive experimental analysis is conducted on geopolymer-based 3D-printed structures, encompassing diverse process parameters associated with 3D printing (3DP) technology. A numerical model is deployed to predict the buildability of these structures, and its results are compared with experimental findings, assessing the efficacy of numerical modeling under varying printing parameter conditions. Rigorous characterization of input material properties is undertaken to ensure the accuracy of numerical simulations. Experimental results showcase favorable processability and buildability of geopolymer materials derived from novel Construction and Demolition Waste (CDW). Findings indicate that an increased nozzle size, reflected in greater layer height and width, positively influences buildability, whereas higher printing speeds correlate with reduced buildability. The numerical model successfully captures these buildability trends, though with an error ranging from 32 % to 45 % in predicting failure in 3D-printed structures. Nevertheless, the overall performance of the numerical model remains reliable in predicting the influence of printing parameters on buildability.
