The possibility of incorporating scrap tire residue into concrete has already been consolidated in previous studies, but there is a knowledge gap about how concrete made with recycled tire materials behaves when exposed to high temperatures. This study aims to investigate the performance of precast concrete panels made with scrap tire residues when exposed to fire when using recycled steel fiber and recycled rubber aggregates separately. The experimental design consisted of fire resistance tests. Real-scale panels were exposed to the standard fire curve based on ISO 834, measuring the temperatures on the panel surfaces. The recycled steel fiber-reinforced concrete and those containing 5% recycled rubber aggregate presented similar behavior when compared to the conventional concrete on thermal insulation, integrity, and structural stability. The concrete made with 10% recycled rubber aggregate registered the occurrence of explosive spalling and worse thermal insulation and integrity.

Aggregate mineralogy and shape effects on concrete mechanical property relationships were evaluated using 114 concrete mixtures that used rounded gravel, crushed gravel, and limestone. Mineralogy (that is, chert versus calcium carbonate) and shape (that is, crushed versus rounded) were found to have a meaningful effect on the relationships between compressive strength (fc), elastic modulus (E), and splitting tensile strength (St). These data sets were used to benchmark several empirical relationships found in the literature to determine their ability to predict E and St based on fc. Most equations from the literature were conservative and did not consider aggregate type. A set of equations, following the form of ACI 318 and a power equation, are recommended by the authors for limestone, crushed gravel, and rounded gravel to realistically predict E and St based on fc.

Recently, alkali-activated cement (AAC) has been studied to partially replace portland cement (PC) to reduce the environmental impact caused by civil construction and the cement industry. However, with regard to durability, few studies have addressed the behavior of AAC. This study aimed to evaluate the performance of AAC made from blast-furnace slag with contents of 4 and 5% sodium hydroxide as an activator (Na2Oeq of 3.72% and 4.42%, respectively) when subjected to alkali-aggregate reaction (AAR). Length variation tests were carried out on mortar bars immersed in NaOH solution (1 N of NaOH, T = 80°C [176°F]) and on concrete bars (T = 60°C [140°F], RH = 95%); compressive strengths tests and scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS) analyses were also made. Two types of PC were used as a comparison. The results showed good behavior of the AAC in relation to the AAR, with expansions lower than those established by the norm (34% of the limit) and without the finding of losses of mechanical resistance or structural integrity. The alkaline activator content had a small influence on the behavior of the AACs, in which the lowest amount of NaOH (4%) showed fewer expansions (only 15% of the limit established by the norm). Even for the highest activator content (5%), the results were good and comparable to those of PC with pozzolans, which is recommended for the inhibition of AAR.

This study investigated the friction shear behavior of concrete consisting of recycled aggregates and natural reinforced steel fibers. The concrete’s natural aggregates were 50% substituted for recycled ones. The addition of steel fibers was evaluated in two different percentages in volume: 0.5 and 1.0%. Thus, 27 non-cracked push-off specimens were produced. The recycled aggregates were separated into two groups according to the strength of the original concrete: Group 1 (15 to 20 MPa) and Group 2 (35 to 40 MPa). Data analysis showed that the concrete’s original strength and steel fiber percentage influenced the shear transfer capacity. Experimental data from natural concrete (NC) and high-strength concrete (HSC) with steel fibers tests performed using the push-off model and shear test methods were recompiled from the technical literature. Using models proposed by some researchers, it was concluded that both methods showed high dispersion in results.

The paper discusses the development and application of the artificial neural network (ANN) model for predicting the compressive and splitting tensile strength of the geopolymer-based concrete composites (GPC). The strength properties of GPC are influenced by the proportions of the constituents—namely, the alkaline solution, fly ash, aggregates, and sand and water—and require optimization for the desired quality of the composite. The optimum mixture may be obtained by using modern techniques; namely ANN modeling. The ANN models have been developed by training and validating the input data using the sigmoid function and the feed-forward backpropagation algorithm in the hidden layers. The ANN layer is the functional part of the model consisting of the operators to carry out the specific task largely based on mathematical calculations. A five-layered ANN model has been developed and used to predict the strength to optimize the mixture design. The predicted values have been compared with the experimental strength values, and the effects of the most significant constituents have been studied.