The production of portland cement is among the major contributors to greenhouse gas emissions that adversely affect the environment. Identifying sustainable materials to partially replace portland cement in concrete, such as pozzolanic materials, is crucial in addressing this issue. These materials mainly consist of silica and alumina that react with the available calcium hydroxide to form strength-bearing phases such as calcium silicate hydrates. Understanding the degree of pozzolanic reactivity of materials using efficient reactivity test methods is an important consideration. The paper thoroughly reviews the available literature related to direct and indirect pozzolanic reactivity test methods that have been utilized over the years. Direct methods quantify the amount of consumed calcium hydroxide, whereas indirect methods assess changes in the physical properties of the specimen due to pozzolanic reactions. The aim of this paper is to identify affordable, time-saving, and effective direct and indirect methods. Based on this study, the Frattini, electrical conductivity, and pH tests are considered the most time-efficient methods to assess pozzolanic materials. Electrical conductivity and pH tests are also easy to perform. In contrast, other methods are more time-consuming.

The performance of both non-spliced and spliced steel bars significantly affects the overall performance of structural reinforced concrete elements. In this context, the mechanical properties of reinforcement bars (i.e., their ultimate strength and strain) should be determined in order to evaluate their reliability prior to the construction procedure. In this study, the application of Tree-Based machine learning techniques is implemented to analyze the ultimate strain of non-spliced and spliced steel reinforcements. In this regard, a database containing the results of 225 experimental tests was collected based on the research investigations available in peer-reviewed international publications. The database included the mechanical properties of both non-spliced and mechanically spliced bars. For better accuracy, the databases of other splicing methods such as lap and welded-spliced methods were excluded from this research. The database was categorized as two sub-databases: training (85%) and testing (15%) of the developed models. Various effective parameters such as splice technique, steel grade of the bar, diameter of the steel bar, coupler geometry—including length and outer diameter along with the testing temperatures—were defined as the input variables for analyzing the ultimate strain using tree-based approaches including Decision Trees and Random Forest. The predicted outcomes were compared to the actual values and the precision of the prediction models was assessed via performance metrics, along with a Taylor diagram. Based on the reported results, the reliability of the proposed ML-based methods was acceptable (with an R² ≥ 85%) and they were time-saving and cost-effective compared to more complicated, time-consuming, and expensive experimental examinations. More importantly, the models proposed in this study can be further considered as a part of a comprehensive prediction model for estimating the stress-strain behavior of steel bars.

This paper deals with analyzing the structural responses of glass-fiber-reinforced polymer (GFRP) tubes filled with recycled and concrete material for developing composite piles, as an alternative to traditional steel reinforced piles in bridge foundations. The full-scale GFRP composite piles included three structural layers, using a fiber-oriented material that was inclined longitudinally. Almost 60% of the fibers were orientated at 35° from the longitudinal direction of the pile and the rest 40 percent were oriented at 86° from the horizontal axis. The segment between the inner and outer layers was inclined 3° from the hoop direction in the tube. The behavior of the filled GFRP tubes was semi-linear and resulted in increasing the total ductility and strength of the piles. Adjusting the material’s properties, such as the E_Axial, E_Hoop, and Poisson ratios, optimized the results. The lateral strength of the GFRP composite pile and pre-stressed piles are investigated under both axial compression and bending moment loads. Based on the conducted parametric study, the required axial and bending capacities of piles in different ranges of eccentricities can be reached using the combination of tube wall thickness and GFRP fiber percentages.

Several advantages of supplementary cementitious materials (SCMs) have led to widespread use in the concrete industry. Many various SCMs with different characteristics are used to produce sustainable concrete. Each of these materials has its specific properties and therefore plays a different role in enhancing the mechanical properties of concrete. Multiple and often conflicting demands of concrete properties can be addressed by using combinations of two or more SCMs. Thus, understanding the effect of each SCM, as well as their combination in concrete, may pave the way for further utilization. This study aims to develop a robust and time-saving method based on Machine Learning (ML) to predict the compressive strength of concrete containing binary SCMs at various ages. To do so, a database containing a mixture of design, physical, and chemical properties of pozzolan and age of specimens have been collected from literature. A total of 21 mix design containing binary mixes of fly ash, metakaolin, and zeolite were prepared and experimentally tests to fill the possible gap in the literature and to increase the efficiency and accuracy of the ML-based model. The accuracy of the proposed model was shown to be accurate and ML-based model is able to predict the compressive strength of concrete containing any arbitrary SCMs at ay ages precisely. By using the model, the optimum replacement level of any combination of SCMs, as well as the behavior of binary cementitious systems containing two different SCMs, can be determined.

Fly ash, as a supplemental pozzolanic material, reduces concrete’s adverse environmental footprint by decreasing the emission of carbon dioxide (CO₂) during the cement manufacturing process. Fly ash, which is a waste material, can enhance both the mechanical characteristics and durability of concrete, and has the capability to play an important role in sustainable design. Considering the widespread interest in applying Fly ash, and despite research studies, the level of replacement is still unclear. In this paper, a novel method using artificial neural networks (ANN) is presented to predict concrete’s mechanical characteristics by adding Fly ash. In this regard, a host of available experimental data, such as the properties of Fly ash, along with concrete additives, was fed into an ANN model. Concrete samples’ tensile and compressive strengths, in addition to their modulus of elasticity, were defined as outputs. It was observed that the predicted outcomes agreed well with the experimental results. To further enhance the research outcomes, simple but practical equations are presented to assess the effect of using Fly ash on concrete’s mechanical characteristics.