The circular economy (CE) has become a trend because concern has arisen regarding the end of life of several products and the reduction of CO₂ emissions in many processes. Since the architecture, engineering, and construction (AEC) industry is one of the biggest generators of environmental impacts, there is a need to apply the CE concept to the industry in order to reduce greenhouse gas (GHG) emissions. However, the role of different tools that are used to integrate CE strategies to reduce GHG emissions by the AEC industry is still unknown in the scientific literature. The purpose of this paper is to carry out a systematic literature review on the theme and analyze the following seven tools: (1) life cycle assessment—LCA; (2) building information modeling—BIM; (3) building environmental certifications—BEC; (4) building materials passports—BMP; (5) waste management plan—WMP; (6) augmented reality—AR; and (7) virtual reality—VR. A total of 30 papers were reviewed, and it was observed that, in terms of CE strategies and climate change mitigation, the vast majority can be classified as closing loops and are mainly related to recycling and reuse at the end of life and the use of recycled materials. Considering the building’s stakeholders, constructors, researchers, and designers can be the main users and, consequently, those that most benefit from the use of the evaluated tools. The integration between LCA, BIM, and BMP was also observed. Finally, as one of the main contributions of this research, other types of integration among the analyzed tools are proposed. These proposals seek to improve and update the tools and also address the need to reduce GHG emissions.

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.