Use of calcium carbonate nanoparticles in production of nano-engineered foamed concrete

Researchers have shown significant interest in the incorporation of nanoscale components into concrete, primarily driven by the unique properties exhibited by these nanoelements. A nanoparticle comprises numerous atoms arranged in a cluster ranging from 10 nm to 100 nm in size. The brittleness of foamed concrete (FC) can be effectively mitigated by incorporating nanoparticles, thereby improving its overall properties. The objective of this investigation is to analyze the effects of incorporating calcium carbonate nanoparticles (CCNPs) into FC on its mechanical and durability properties. FC had a 750 kg/m3 density, which was achieved using a binder-filler ratio of 1:1.5 and a water-to-binder ratio of 0.45. The CCNPs material exhibited a purity level of 99.5% and possessed a fixed grain size of 40 nm. A total of seven mixes were prepared, incorporating CCNPs in FC mixes at the specific weight fractions of 0% (control), 1%, 2%, 3%, 4%, 5%, and 6%. The properties that were assessed included the slump, bulk density, flexural strength, splitting tensile strength, compressive strength, permeable porosity, water absorption, drying shrinkage, softening coefficient, and microstructural characterization. The results suggested that incorporating CCNPs into FC enhanced its mechanical and durability properties, with the most optimal improvement observed at the CCNPs addition of 4%. In comparison to the control specimen, it was witnessed that specimens containing 4% CCNPs demonstrated remarkably higher capacities in the compressive, splitting tensile, and flexural tests, with the increases of 66%, 52%, and 59%, respectively. The addition of CCNPs resulted in an improvement in the FC porosity and water absorption. However, it also led to a decrease in the workability of the mixtures. Furthermore, the study provided the correlations between the compressive strength and splitting tensile strength, as well as the correlations between the compressive strength and flexural strength. In addition, an artificial neural network approach was employed, utilizing k-fold cross validation, to predict the compressive strength. The confirmation of the property enhancement was made through the utilization of a scanning electron microscope. & COPY; 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).