Data on C-A-S-H (calcium aluminate silicate hydrate) thermoelastic properties is a critical input for multiscale approaches aiming at understanding the thermo-mechanical behavior of cement systems with partial substitution of ordinary Portland cement with supplementary cementitious materials. Molecular simulations at finite temperature are deployed to compute the elastic constants, coefficient of thermal expansion, and heat capacity of C-A-S-H. The heat capacity is calculated following a recently proposed hybrid approach well-suited for hydrated minerals. With this approach, quantum corrections are added for the dry mineral, whereas confined water heat capacity is accounted for through full classical computations. The coefficient of thermal expansion of C-A-S-H is estimated for the first time. Using homogenization theory, the effective properties are computed at the gel scale as a function of the packing density using as input the properties computed at the molecular scale. Our results show good agreement with available experimental data.

The purpose of this study is to develop a prediction method for the durability of reinforced concrete against salt attacks for any mix proportions and environmental conditions. To accomplish this, we investigate the effects of concrete composition and water content on resistivity and then model resistivity for any aggregate volume fractions using a modification of McCarter's resistivity model of concrete. We separately model the resistivity of cement paste, taking into account the effective amount of liquid water in the capillary and gel pores and the structure of C-S-H. This is incorporated into the proposed resistivity model. As a result, it is possible to predict the risk of corrosion due to salt attacks for a range of concrete compositions and environmental factors. In particular, it has become possible to model the corrosion resisting effect of using blast furnace slag as a mineral admixture. Further, it has become possible to determine the corrosion risk in consideration of the length of the drying periods between times of wetting.

Recycled aggregate concrete (RAC) has attracted more interesting in the past several years because it is an economical and eco-friendly building material. But generally, the mechanical properties of RAC are poor compared to natural aggregate concrete (NAC). So, the mechanical properties of RAC need robust predictive models to be evaluated before its application. Traditional (empirical based) models, e.g., linear, and non-linear regression methods, have been extensively proposed. But these models lack flexibility in updating (i.e., limited to a finite number of variables) and can give inaccurate results. Consequently, to handle such shortcomings, several Artificial Intelligence (AI) models have been suggested as an alternative strategy for predicting the mechanical properties of RAC. In this study, state-of-the-art AI models were reviewed to predict the mechanical properties of RAC. The application of each predictive model and its training, testing, and performance are critically examined and analysed, consequently identifying present knowledge gaps, practical recommendations, and required future investigation.