Use of calcium carbonate nanoparticles in production of nano-engineered foamed concrete
Md Azree Othuman Mydin, P. Jagadesh, Alireza Bahrami, Anmar Dulaimi, Yasin Onuralp Özkılıç, Mohd Mustafa Al Bakri Abdullah, Ramadhansyah Putra Jaya
Abstract
Researchers have shown significant interest in the incorporation of nanoscale components into concrete, primarily driven by the unique properties exhibited by these nano elements. A nanoparticle comprises numerous atoms arranged in a cluster ranging from 10 to 100 nanometers 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 (CCNP) into FC on its mechanical and durability properties. The 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 CCNP material exhibited a purity level of 99.5% and possessed a fixed grain size of 40nm. A total of seven mixes have been prepared, incorporating CCNP in FC mixes at specific weight fractions of 0% (control), 1%, 2%, 3%, 4%, 5%, and 6%. The properties that were assessed include slump, bulk density, flexural strength, splitting tensile strength, compressive strength, permeable porosity, water absorption, drying shrinkage, softening coefficient, and microstructural characterization. The results suggest that incorporating CCNP into FC enhances its mechanical and durability properties, with the most optimal improvement observed at a CCNP addition of 4%. In comparison to the control specimen, it was observed that specimens containing 4% CCNP demonstrated significantly higher capacities in compressive, splitting tensile, and flexural tests, with increases of 66%, 52%, and 59% respectively. The addition of CCNP 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 also provided the correlations between compressive strength and splitting tensile strength, as well as the correlations between compressive strength and flexural strength. In addition, an artificial neural network (ANN) approach was employed, utilizing k-fold cross-validation, to predict the compressive strength. The confirmation of property enhancement is verified through the utilization of a Scanning Electron Microscope.