Influence of phase change material and nano silica aerogel aggregates on the characteristics of cementitious composite: An experimental and predictive study

This paper studies the influence of two high-performance thermal insulating materials including aerogel and phase change materials (PCM) aggregates, on the key characteristics of cementitious composites in experimental and predictive aspects. Thermal conductivity (ThC), rapid chloride migration test (RCMT), permeable pore volume, compressive strength, dry density, and microstructural analysis were performed for the mechanical properties. Moreover, Least Squares Support Vector Regression (LSSVR) as a machine learning method was introduced to evaluate and provide a proper method for non-destructive evaluation of parameters. The results of experiments indicated that both aerogel and PCM aggregates were highly effective although the aerogel showed more efficiency in enhancing the thermal insulation property of cementitious mixtures. In addition, the compressive strength of the mixtures with PCM composites was higher than the aerogel aggregates, where the compressive strength of the mixture containing 20 % paraffin was 50 % higher than the aerogel mixture with similar incorporation levels. Moreover, the mixtures with PCM aggregates had significantly lower water and chloride permeability compared to those of plain mixtures, and mixtures consisted of aerogel aggregates. The scanning electron microscope (SEM) analyses, also, revealed that the use of the aerogel aggregates led to an open and porous structure of the mixtures; however, PCM aggregates had a weak interfacial transition zone (ITZ) in the vicinity of cement paste. Furthermore, LSSVR model provided a close prediction of the effective parameters with an acceptable accuracy in capturing the influences of important parameters. •Aerogel and paraffin incorporation lowers compressive strengths.•Aerogel and paraffin effectively reduce thermal conductivity with aerogel outperforming paraffin at higher levels.•LSSVR model was able to predict the effective parameters with an acceptable accuracy.