The work presents the results of an experimental campaign carried out on concrete elements in order to investigate the potential of using artificial neural networks (ANNs) to estimate the compressive strength based on relevant parameters, such as the water–cement ratio, aggregate–cement ratio, age of testing, and percentage cement/metakaolin ratios (5% and 10%). We prepared 162 cylindrical concrete specimens with dimensions of 10 cm in diameter and 20 cm in height and 27 prismatic specimens with cross sections measuring 25 and 50 cm in length, with 9 different concrete mixture proportions. A longitudinal transducer with a frequency of 54 kHz was used to measure the ultrasonic velocities. An ANN model was developed, different ANN configurations were tested and compared to identify the best ANN model. Using this model, it was possible to assess the contribution of each input variable to the compressive strength of the tested concretes. The results indicate an excellent performance of the ANN model developed to predict compressive strength from the input parameters studied, with an average error less than 5%. Together, the water–cement ratio and the percentage of metakaolin were shown to be the most influential factors for the compressive strength value predicted by the developed ANN model.

This work aims to study the influence of using construction and demolition waste in the replacement of coarse and fine aggregate to produce recycled aggregate concrete (RAC). A moderate compressive strength concrete made with usual fine and coarse aggregate was used as a benchmark material. Compressive and split tensile tests were performed using 120 cylindrical concrete specimens with 150 mm diameter and 300 mm length. Four-point flexural tests in reinforced beams made with conventional concrete and RAC were performed. The results obtained showed that the use of recycled fine aggregates, in both percentages of substitution investigated—50% and 100%—did not generate any deleterious influence on the values of compressive strength and split tensile strength of the RACs produced. Tin fact, the mechanical strengths of RACs produced with recycled fine aggregate were equal or higher than those from the reference concrete. The same behavior was not observed, however, when the recycled coarse aggregate was used. For this case, decreases in concrete mechanical strengths were observed, especially in compressive strength, with values around 35% lower when compared to the reference concrete. Tensile mechanical tests results confirmed the excellent behavior of all RACs made with replacement of usual fine aggregates by recycled. Bending tests performed in reinforced RAC beams had as objective to evaluate the deformation profile of the beams. The obtained results showed that RAC beams with full replacement of usual fine aggregate by the recycled aggregates have presented little changes in the global behavior, an aspect that encourages its use.

Despite technological advances in the production processes of the materials for ceramic façade coatings, the problems of detachments are still frequent. Therefore, this work aims to investigate, through a literature review, the existing gaps related to the adhesion ability of adhesive mortars, identifying new research needs that can better explain the behavior of the material. In addition, an experimental procedure was developed to evaluate the mechanical capacity of adhesive mortars when subjected to cyclic stresses. Dynamic stress measurements are presented for several blocks of mortar and on similar blocks but with a slot drilled prior to measurements (intended to represent failure). From these data we calculated values of stress energy, elastic energy, and dissipated energy. The experimental results showed that the energy involved in the test process accompanied the load values and current stress values. The mortar samples with the previous failure absorbed and dissipated less energy than mortars without failure, showing that materials that have less energy to dissipate, are materials that have developed less capacity to adhere, that is, to keep their parts together.