PERFORMANCE ASSESSMENT OF 3D PRINTED AUXETIC CEMENTITIOUS CELLULAR COMPOSITES

Auxetic behavior implies a negative Poisson’s ratio whereby the specimen contracts instead of expands in transversal direction in a compression test. This novel response results from a special arrangement of voids formed by auxetic structures inside the sample. Combined with the advantages of concrete such as low costs, availability, strength in compression, durability, and fire resistance this innovative material of auxetic cementitious cellular composites (ACCC) provides a lot of new opportunities for the building industry. With the advent of 3D printing technology, it is now possible to achieve performance enhancement by tuning the geometrical architecture in the mesoscale. In this research different variations of auxetic mortar structures were investigated and a procedure to create auxetic test specimen was developed. New auxetic geometries applicable for a brittle material like mortar were generated. Molds with these structures were printed with a 3D printer to cast the mortar and after 28 days the samples were tested in compression and three-point bending tests. The digital image correlation (DIC) method was used to determine the displacements during the experiments as these are necessary to identify auxetic behavior. For this method a new technique including a handheld printer was developed to improve the procedure of painting the required black dots on the specimen. Analyzing the strength and auxetic behavior of the different structures, the modified re-entrant honeycomb structure was identified to be the most promising geometry for an application in the building industry. As a verification of the results and for defining requirements for auxetic behavior, FEA simulations were performed for CCCs with elliptical voids judiciously oriented to achieve auxetic behavior. The FEA simulation results suggest that a high number of elliptical voids and a long major axis are necessary to achieve a negative Poisson’s ratio. Besides, high aspect ratios of voids can enhance the auxetic behavior. The lowest Poisson’s ratio found was -0.33 and the corresponding geometry can be used for future investigations. The discovered geometries can be used as a first step towards a varied and very auspicious application of ACCCs in civil engineering. The developed FEA-based simulation framework is used to generate a large, consistent dataset for auxetic CCCs with varying mesoscale architectural features. Such dataset can be leveraged to develop robust and efficient machine learning (ML)-based performance prediction tools in the future which would be of enormous value to the materials designers and decision-maker