International Concrete Abstracts Portal

This study employs machine learning (ML) to predict ultrasonic pulse velocity (UPV) based on the mix composition and curing conditions of concrete. A dataset was compiled using 1495 experimental tests. Extreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) were applied to predict UPV in both direct and surface transmissions. The Monte Carlo approach was used to assess model performance under input fluctuations. Feature importance analyses, including the Shapley Additive Explanation (SHAP), were conducted to evaluate the influence of input variables on wave propagation velocity in concrete. Based on the results, XGBoost outperformed SVR in predicting both direct and surface UPV. The accuracy of the XGBoost model was reflected in average R² values of 0.8724 and 0.9088 for direct and surface UPV, respectively. For the SVR algorithm, R² values were 0.8362 and 0.8465 for direct and surface UPV, respectively. In contrast, linear regression exhibited poor performance, with average R² values of 0.6856 and 0.6801 for direct and surface UPV. Among the input features, curing pressure had the greatest impact on UPV, followed by cement content. Water content and concrete age also demonstrated high importance. In contrast, sulfite in fine aggregates and the type of coarse aggregates were the least influential variables. Overall, the findings indicate that ML approaches can reliably predict UPV in healthy concrete, offering a useful step toward more precise health monitoring through the detection of UPV deviations caused by potential damage.

Biochar properties, in particular, its fineness and its ability to absorb water, can be exploited to modify the rheological behaviour of cementitious conglomerates and to improve the hydration of the cement paste under adverse curing conditions such as those related to 3D concrete printing. Regarding the fresh state properties, the study of the rheological properties conducted on cementitious pastes for different biochar additions (by weight of cement: 0%, 1.5%, 2%, and 3%) highlights that the biochar induces an increase in yield stress and plastic viscosity. The investigation of mechanical properties, in particular flexural sand compressive strength, performed on mortars, evidences the internal curing effect promoted by biochar additions (by weight of cement: 0%, 3%, and 7.7%). In fact, compared to the corresponding specimens cured for the first 48 hours in the formwork, specimens with biochar addition cured directly in air are characterised by a drastically lower reduction in compressive strength than the reference specimens, i.e., approximately 36% and 48% respectively. This interesting result can also be exploited in traditional construction techniques, where faster demolding is needed.

The production of carbonated aggregates from Class F fly ash (FA) is challenging due to its low calcium content, typically less than 10%. This study investigates the production of carbonated alkali-activated aggregates using FA and calcium carbide sludge (CCS). Sodium hydroxide was used as an activator and examined the effects of autoclave treatment on the properties of these aggregates. The optimal mixture, comprising 70% FA and 30% CCS, achieved a single aggregate strength of >5 MPa in autoclave carbonated (AC) aggregates, comparable to the strength obtained after 14 days of water curing in without autoclave carbonated (WAC) aggregates. Both AC and WAC aggregates exhibited a bulk density of 790 to 805 kg/m³ and CO₂ uptake of 12.5% and 13.3% in AC and WAC aggregates, respectively. FE-SEM and FT-IR analysis indicated the formation C-A-S-H gel in noncarbonated aggregates, while calcite and vaterite, along with N-A-S-H gel, formed in carbonated aggregate. Concrete incorporating AC and WAC aggregates exhibit compressive strengths of 39 and 38 MPa, with concrete density of 2065 kg/m³ and 2085 kg/m³, respectively. Furthermore, AC and WAC aggregate concrete showed a reduction in CO₂ emission of 18% and 31%, respectively, compared to autoclave noncarbonate (ANC) aggregate concrete. These findings highlight the potential of producing carbonated alkali-activated aggregates from FA and CCS as sustainable materials for construction applications.

Geopolymer concrete (GPC) is a progressive material with the capability to significantly reduce global industrial waste. The combination of industrial by-products with alkaline solutions initiates an exothermic reaction, termed geopolymerization, resulting in a carbon-negative concrete that lessens environmental impact. Fly ash (FA)-based GPC displays noticeable variability in its mechanical properties due to differences in mixture design ratios and curing methods. To address this challenge, the authors optimized the constituent proportions of GPC through a meticulous selection of nine independent variables. A thorough experimental database of 1242 experimental observations was assembled from the available literature, and artificial neural networks (ANNs) were employed for compressive strength modeling. The developed ANN model underwent rigorous evaluation using statistical metrics such as R-values, R2 values, and mean squared error (MSE). The statistical analysis revealed an absence of a direct correlation between compressive strength and independent variables, as well as a lack of correlation among the independent variables. However, the predicted compressive strength by the developed ANN model aligns well with experimental observations from the compiled database, with R2 values for the training, validation, and testing data sets determined to be 0.84, 0.74, and 0.77, respectively. Sensitivity analysis identified curing temperature and silica-to-alumina ratio as the most crucial independent variables. Furthermore, the research introduced a novel method for deriving a mathematical expression from the trained model. The developed mathematical expressions accurately predict compressive strength, demonstrating minimal errors when using the tan-sigmoid activation function. Prediction errors were within the range of –0.79 to 0.77 MPa, demonstrating high accuracy. These equations offer a practical alternative in engineering design, bypassing the intricacies of the internal processes within the ANN.

This study investigates the impact of air-entraining admixtures (AEAs) on mortar performance, focusing on fresh-state and hardened-state properties critical to durability and engineering applications. Ten distinct mortar mixtures were analyzed, following guidelines established by the European Federation of National Associations Representing Producers and Applicators of Specialist Building Products for Concrete (EFNARC). AEAs were introduced at varying proportions (0.01 to 0.5% of cement weight) to evaluate their effects on intrinsic properties (density, void ratio, and water absorption), rheological parameters (plastic viscosity and yield stress), and mechanical characteristics (compressive strength, ultrasonic velocity, and modulus of elasticity). Regression models were developed and yielded high predictive accuracy, with R2 values exceeding 0.98. Notably, ultrasonic velocity and modulus of elasticity demonstrated strong correlations with intrinsic properties across all curing ages. Similarly, compressive strength showed significant associations with rheological parameters, highlighting the influence of air content and flow behavior on structural performance. These findings offer precise quantitative models for predicting mortar behavior and optimizing formulations for enhanced performance.