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.

Given that steel is one of the main structural materials in civil engineering, it is important to understand its mechanical properties and failure mechanisms. L-shaped laminated steel elements, known as angles, are commonly bolted to other metal elements to make connections in steel structures. When these elements are connected by only one of its legs and are subjected to axial tension, the failure of the angle can be caused by rupture of the section where the holes are located. In this case, there is influence of complex phenomena such as shear lag, therefore a reduction factor is applied to the resistance of the section. The general theme of this work is the use of Artificial Neural Networks (ANN) as an approach to the study of the load capacity of cold-formed steel bolted angles connected by one leg and under axial tension. Although this class of techniques do not provide a simple regression equation, they are powerful tools in Machine Learning (ML), having the ability to model any arbitrarily complex nonlinear problem. In this work, a dataset is built with samples from different numerical and experimental works, collecting steel angles’ geometric parameters, tensile strength of the material and ultimate capacity regarding net section failure. The data-driven models are trained with 80% of the data, tested with 20%, validated with 5-fold cross-validation and their hyperparameters are tuned with Bayesian Optimization. Feature generation, selection, and importance algorithms are implemented, hoping to achieve more accurate, less complex and more interpretable models. ML models’ predictions are compared with the ones given by the equations of Eurocode-3, Brazilian NBR 14762, American Iron and Steel Institute (AISI) and Australasian AS/NZS Standards. The comparison shows mostly superior results in the ANN models, proving the competitiveness and effectiveness of Machine Learning techniques and data-driven modeling.