Analysis of the Microstructure and Porosity of Cement Pastes with Functionalized Nanosilica with Different Contents of Aminosilane.

This research aims to analyze the effect of functionalized nanosilica (NSF) with different levels of amine groups in the formation of hydration products. Four cement pastes were investigated, one reference with Portland cement and three replacing 1% of Portland cement by nanosilica (NS), NSF with a low content of amine groups, and NSF with a high content of amine groups. The heat of hydration of the pastes was evaluated up to 7 days of hydration, the amount of calcium hydroxide (CH) and hydrated phases by means of the thermogravimetric analysis (TGA) test and compressive strength at 2, 7, and 28 days, and porosity through tests of mercury intrusion porosimetry and computed tomography at 28 days of hydration. It was possible to observe that the NSF directly influenced the hydration kinetics of the pastes, delaying the hydration of the Portland cement; however, it demonstrated a similar mechanical performance to the paste with NS at 2 days of hydration and an increase of 10% at 28 days of hydration due to the improvement in the hydration process. Thus, it is possible to conclude that the functionalization of NSF with a low 3-aminopropyltriethoxysilane (APTES) content is promising for use in cementitious materials and may improve hydration and mechanical performance at more advanced ages compared to NS.

Machine Learning-Based Prediction of the Compressive Strength of Brazilian Concretes: A Dual-Dataset Study.

Lately, several machine learning (ML) techniques are emerging as alternative and efficient ways to predict how component properties influence the properties of the final mixture. In the area of civil engineering, recent research already uses ML techniques with conventional concrete dosages. The importance of discussing its use in the Brazilian context is inserted in an international context in which this methodology is already being applied, and it is necessary to verify the applicability of these techniques with national databases or what is created from national input data. In this research, one of these techniques, an artificial neural network (ANN), is used to determine the compressive strength of conventional Brazilian concrete at 7 and 28 days by using a database built through publications in congresses and academic works and comparing it with the reference database of Yeh. The data were organized into nine variables in which the data samples for training and test sets vary in five different cases. The eight possible input variables were: consumption of cement, blast furnace slag, pozzolana, water, additive, fine aggregate, coarse aggregate, and age. The response variable was the compressive strength of the concrete. Using international data as a training set and Brazilian data as a test set, or vice versa, did not show satisfactory results in isolation. The results showed a variation in the five scenarios; however, when using the Brazilian and the reference data sets together as test and training sets, higher R2 values were obtained, showing that in the union of the two databases, a good predictive model is obtained.