Concrete is classified as a multi-composite material comprising three phases: coarse aggregate, mortar, and interfacial transition zone (ITZ). Fine and coarse aggregates occupy approximately 70–85% by volume, of which coarse aggregate typically constitutes more than two-thirds of the total quantity of aggregate by volume. The current study investigates the concrete performance produced using various recycled construction and by-product industrial waste coarse aggregates. Six types of coarse aggregates: manufactured limestone, quartzite, natural scoria, by-product industrial waste aggregate, and two sources of recycled concrete aggregates with densities ranging from 860 to 2300 kg/m³ and with different strength properties were studied. To determine the coarse aggregate contribution to the overall concrete performance, lean and rich concrete mixtures (Mix 1 and Mix 2) were used. Mix 1 (lean mixture) consisted of a ratio of water to cement (w/c) of 0.5 and cement content of 300 kg/m³, whereas a higher quantity of cement of 500 kg/m³ and a lower w/c ratio of 0.3 were used for Mix 2 (rich mixture). The results showed that while the compressive strength for different aggregate types in Mix 1 was comparable, the contribution of aggregate to concrete performance was very significant for Mix 2. Heavyweight aggregate produced the highest strength, while the lightweight and recycled aggregates resulted in lower mechanical properties compared to normal weight aggregates. The modulus of elasticity was also substantially affected by the coarse aggregate characteristics and even for Mix 1. The ACI 363R-92 and CSA A23.3-04 appeared to have the best model for predicting the modulus of elasticity, followed by the ACI-318-19 (density-based formula) and AS-3600-09. The density of coarse aggregate, and hence concrete, greatly influenced the mechanical properties of concrete. The water absorption percentage for the concrete produced from various types of aggregates was found to be higher for the aggregates of higher absorption capacity.

Recycling rubber and/or steel fiber components of waste tires in construction applications is a venue for maximizing the recycling rate of these items. Additionally, it supports the move towards producing sustainable construction materials and conserving natural resources. Previous research explored the viability of employing recycled waste rubber particles as an alternative for natural aggregate. Despite the adverse effect of rubber on the mechanical properties of concrete (e.g., lower compressive strength), it produces several advantages, including excellent dynamic and ductility properties, which can be utilized in structural members critical to dynamic loads, e.g., blasts, earthquakes, and impacts. In an effort to expand the adoption of waste rubber in concrete beams and to eliminate key concerns associated with the degradation of their flexural behavior, the functionally graded (FG) beams concept was utilized. The present investigation comprised the testing of five beams using a four-point bending configuration. Plain concrete, rubberized concrete (RuC), and steel-fiber reinforced rubberized concrete (SFRRuC) beams were cast along with FG beams arranged in two layers. The top layer of the FG beams comprised plain concrete, while the bottom layer consisted of RuC or SFRRuC. Experimental findings indicated that the flexural behavior of the FG beam with layers of SFRRuC and plain concrete exceeded the flexural strength, displacement ductility ratio, and toughness performances of the plain concrete beam by 9.9%, 12.9%, and 24.4%, respectively. The moment–curvature relationship was also predicted for the tested beam and showed an excellent match with the experimentally measured relationship.

Existing machine learning (ML) models often encounter challenges in accurately predicting the shear strength of steel fiber reinforced concrete (SFRC) beams, mainly due to a lack of generalization. This study introduces an advanced stacked ensemble ML architecture to overcome this limitation by utilizing a comprehensive data set of 394 experimental observations and a 20-feature matrix. The model exhibits exceptional performance with a mean absolute error of 0.391 and a correlation coefficient ($R^2$) of 93.7%, and surpasses traditional ML algorithms. Furthermore, a sensitivity analysis of the developed model yields that shear strength is highly responsive to the shear span-to-effective depth ratio, with an increase from 1 to 4 resulting in a significant reduction (about 50%) in strength. Increasing the percentage of longitudinal steel from 1 to 2% leads to a 14.6% gain, whereas doubling its yield strength has a more modest 3.7% effect. Increasing the compressive strength of concrete from 25 to 50 MPa, notably increases the shear strength by 19.6%. Fiber length, diameter, and aspect ratio exhibit varying impacts, with shear strength most influenced by the fiber volume fraction, which leads to a peak enhancement of 30.7% at 2% fibrous volume; however, the tensile strength of fibers minimally affects the shear strength. Additionally, this research presents a simplified empirical model to predict the shear strength of SFRC beams based on the key determinants. This model employs the iterative Gauss–Newton algorithm, demonstrates reasonable predictive capability, and boasts an $R^2$ of 83.3% and mean prediction-tested strengths of around 1.039. The practical implications of these findings are substantial for the construction industry as they enable a more accurate and reliable design of SFRC beams, optimize material usage, and potentially reduce construction costs as well as enhance structural safety.

The utilization of manufactured lightweight aggregates adds another dimension to the cost of the preparation of self-compacting concrete (SCC). The common practice of adding absorption water to the lightweight aggregates before concreting leads to inaccurate calculations of the water-to-cement ratio. Moreover, the absorption of water weakens this interfacial bond between aggregates and the cementitious matrix. A particular type of black volcanic rock with a vesicular texture known as scoria rocks (SR) is utilized. With an adapted sequence of additions, the occurrence of water absorption can be minimized to overcome the issue of calculating the true water content. In this study, the approach of preparing the cementitious paste first with adjusted rheology followed by the addition of fine and coarse SR aggregates enabled us to circumvent the need for adding absorption water to the aggregates. This step has improved the overall strength due to the enhanced bond between the aggregate and the cementitious matrix, rendering a lightweight SCC mix with a target compressive strength of 40 MPa at 28 days, which makes it appropriate for structural applications. Different mixes were prepared and optimized for the best cementitious system that achieved the goal of this study. The optimized quaternary cementitious system included silica fume, class F fly ash, and limestone dust as essential ingredients for low-carbon footprint concrete. The rheological properties and parameters of the optimized mix were tested, evaluated, and compared to a control mix prepared using normal-weight aggregates. The results showed that the optimized quaternary mix satisfied both fresh and hardened properties. Slump flow, T50, J-ring flow, and average V-funnel flow time were in the ranges of 790–800 mm, 3.78–5.67 s, 750–780 mm, and 9.17 s, respectively. Moreover, the equilibrium density was in the range of 1770–1800 kg/m³. After 28 days an average compressive strength of 42.7 MPa, a corresponding flexural load of over 2000 N, and a modulus of rupture of 6.2 MPa were obtained. The conclusion is then drawn that altering the sequence of mixing ingredients becomes a mandatory process with scoria aggregates to obtain high-quality lightweight concrete for structural applications. This process leads to a significant improvement in the precise control of the fresh and hardened properties, which was unachievable with the normal practice used with lightweight concrete.