# Concrete Structures: Latest Advances and Prospects for a Sustainable Future
## 2 An Overview of Published Articles
It has been estimated that the concrete industry produces 14 billion cubic meters of concrete every year [15], to which approximately 8% of the global emissions of carbon dioxide are connected. Thus, a new challenge of the research is the development of strategies capable of reducing the level of harmful emissions for environmental purposes. A promising approach is to replace conventional cement ingredients with waste materials that are also advantageous for reducing waste and improving their management. Moreover, they may also represent a quite promising approach for developing countries, and contributions 9 and 10 deal with this topic. Contribution 9 discusses a procedure to prepare so-called 'green concrete', i.e., a geopolymer, and cement mortars using mineral wool waste. In the study, durability tests were performed to estimate the relationship between the amount of mineral wool and the flexural and compressive strength of mortars with the aim of determining if the immobilisation of mineral wool in the geopolymer allows to reduce the leaching of phenol and formaldehyde emissions to the environment. Moreover, the authors highlighted that the use of the highest cement-to-wood content ratio increases compressive strength. Moreover, geopolymer mortars show better flexural strength compared to cement mortars. The authors proved that immobilization of the wool in the geopolymer allowed them to significantly reduce phenol and formaldehyde leaching. Contribution 10 investigated the effects of a partial replacement of ordinary Portland cement with cow dung ash (CDA) in mortar mixes. The authors evaluated the changes on workability, water absorption, bulk density, compressive strength, homogeneity, surface attack resistance, thermal decomposition, and mineralogical composition. The study highlighted that as the percentage of CDA content increased over the 5%, the mortar's workability reduced and affected its density and compressive strength. An important topic in the field of the maintenance and management of r.c. structures is the knowledge of the effects of some commonly adopted maintenance tasks on the durability of structures and on their safety level. In contribution 11, the authors dealt with this topic and investigated the effects of the use of de-icing salts in bridge decks on the corrosion of steel rebars and on post-tensioning elements. The paper highlighted that in concrete sensitive to an aggregate-alkali reaction, internal stresses may occur, thus causing a high number of cracks. These cracks, in conjunction with the lack of an adequate waterproof treatment, may cause chlorides to enter from de-icing salts. Therefore, the macroscopic appearance of a brittle fracture of transverse reinforcements can be observed. These pathologies may affect the safety level of bridges and the costs of structural maintenance in a bridge deck, along with the use of de-icing salts. The issue is also explored by analysing a bridge, which was repaired in 2020. The execution of high-performance waterproof treatments during the construction phases can play a key role in avoiding the evolution of the previously mentioned pathologies and can reduce their effects on structural safety. Existing structures that are not waterproof are also discussed. Contemporary architecture is characterised by complex geometrical features, whose realisation requires structural materials able to be adapted to such geometries and with high strengths. Based on these demands, self-compacting high-strength cement-based materials can represent an ideal solution. In contribution 12, this topic is investigated with the aim of identifying models able to optimise the performance of self-compacting mortars. The study focuses on fresh state properties and strength development. The authors used a statistical response surface methodology, which was adopted for the analysis of a database that included a total of thirty formulations of self-compacting mortars with four quantitative input variables.