The efficiency of the mechanochemical activation (MCA) is influenced by various process parameters as well as by the properties of the treated material. The main objective of this research was to optimize the MCA process, gaining enhancement of the chemical reactivity of a Swedish raw clay, which is going to be used as an alkali-activated cementitious binder. The effects of the amount of water, the filling ratio, the rotation speed, and the grinding duration on the amorphization degree were evaluated by X-Ray Diffraction (XRD) and Scanning Electron Microscopy (SEM). Generally, wet and dry processes showed an extensive amorphization of both kaolinite and muscovite minerals present in the studied clay. On the contrary, quartz was amorphized mainly by the wet grinding process. The efficiency of both dry and wet grinding processes was enhanced by the increased number of grinding media versus the amount of the activated material. However, longer processing times caused significant agglomeration while a higher rotational speed enhanced the amorphization. Preliminary tests have shown that alkali activation of the processed clays produced hardened samples. Furthermore, the increased amorphization corresponded to the increased compressive strength values.

Developing accurate and interpretable models to forecast concrete’s self-healing behavior is of interest to material engineers, scientists, and civil engineering contractors. Machine learning (ML) and artificial intelligence are powerful tools that allow constructing high-precision predictions, yet often considered “black box” methods due to their complexity. Those approaches are commonly used for the modeling of mechanical properties of concrete with exceptional accuracy; however, there are few studies dealing with the application of ML for the self-healing of cementitious materials. This paper proposes a pioneering study on the utilization of ML for predicting post-fire self-healing of concrete. A large database is constructed based on the literature studies. Twelve input variables are analyzed: w/c, age of concrete, amount of cement, fine aggregate, coarse aggregate, peak loading temperature, duration of peak loading temperature, cooling regime, duration of cooling, curing regime, duration of curing, and specimen volume. The output of the model is the compressive strength recovery, being one of the self-healing efficiency indicators. Four ML methods are optimized and compared based on their performance error: Support Vector Machines (SVM), Regression Trees (RT), Artificial Neural Networks (ANN), and Ensemble of Regression Trees (ET). Monte Carlo analysis is conducted to verify the stability of the selected model. All ML approaches demonstrate satisfying precision, twice as good as linear regression. The ET model is found to be the most optimal with the highest prediction accuracy and sufficient robustness. Model interpretation is performed using Partial Dependence Plots and Individual Conditional Expectation Plots. Temperature, curing regime, and amounts of aggregates are identified as the most significant predictors.

The aim of this work was to develop an efficient approach to prepare a macroporous scaffold for sophisticated bone replacement, avoiding a long-lasting and complex methodology. Such a scaffold based on the 3-(trimethoxysilyl)propyl methacrylate–POSS hybrid was synthesized via the reaction of 3-(trimethoxysilyl)propyl methacrylate and the trifluoromethanesulfonate–POSS salt. The results show that the chemical composition, structural dimensions, topography, and microstructural properties of the scaffold fulfill the potential requirements for hard-tissue engineering. The microstructural properties were evaluated with the use of X-ray microcomputed tomography (micro-CT) and nanoindentation tests. The former makes it possible to estimate the geometrical measures of the microstructure (porosity, thickness distribution, etc.), whereas the latter makes it possible to estimate the mechanical properties of the constituents of the material (hardness, stiffness modulus, creep, etc.). The aforementioned laboratory testing methods are modern techniques, currently being developed for materials science, making it possible to determine the microstructural/measures of the analyzed system.

An efficient solution to increase the sustainability of building materials is to replace Portland cement with alkali-activated materials (AAM). Precursors for those systems are often based on water-cooled ground granulated blast furnace slags (GGBFS). Quenching of blast furnace slag can be done also by air but in that case, the final product is crystalline and with a very low reactivity. The present study aimed to evaluate the cementitious properties of a mechanically activated (MCA) air-cooled blast furnace slag (ACBFS) used as a precursor in sodium silicate alkali-activated systems. The unreactive ACBFS was processed in a planetary ball mill and its cementing performances were compared with an alkali-activated water-cooled GGBFS. Mixes based on mechanically activated ACBFS reached the 7-days compressive strength of 35 MPa and the 28-days compressive strength 45 MPa. The GGBFS-based samples showed generally higher compressive strength values.