International Concrete Abstracts Portal

This study comprehensively investigates the development of ambient-cured self-compacting geopolymer concrete (SCGC) based on the chemical composition of binder and alkaline activator. Five factors of the chemical composition of binder and alkaline activator, each with four levels, are used to evaluate and optimise the workability and compressive strength of the high-strength SCGC. The designed SCGC mixes provided sufficient workability properties and compressive strength between 28 MPa [4061 psi] and 70.3 MPa [10196 psi]. It was found that the SCGC mix with a binder content of 600 kg/m³ [37.4 lb/ft³], a CaO/(SiO₂+Al₂O₃) mass ratio of 0.55, a Na2O/binder mass ratio of 0.11, a SiO₂/Na₂O mass ratio of 1.2, and Na₂O/H₂O mass ratio of 0.35 was the optimum mix, which achived slump flow of 770 mm [30.3 in.], 28-day compressive strength of 70.3 MPa [10196 psi], and final setting time of 80 min. The CaO/(SiO₂+Al2O₃) ratio in binders, binder content, and Na₂O/binder mass ratio have been found to be the most influential factors on the workability and compressive strength of ambient-cured SCGC. Microstructure analysis of SCGC mixes showed that the increase in the CaO/(SiO₂+Al₂O₃) ratio promoted the formation of calcium-aluminate-silicate-hydrate (C-A-S-H) gels and enhanced the compressive strength by filling voids and creating a compact and dense microstructure.

Biochar properties—in particular, its fineness and ability to absorbwater—can be exploited to modify the rheological behavior ofcementitious conglomerates and improve the hydration of cementpaste under adverse curing conditions, such as those related tothree-dimensional (3-D) concrete printing. Regarding the freshstateproperties, the study of rheological properties, conductedon cementitious pastes for different biochar additions (by weightof cement: 0, 1.5, 2, and 3%), highlights that the biochar inducesan increase in yield stress and plastic viscosity. The investigationof mechanical properties—in particular, flexural and compressivestrength—performed on mortars evidences the internal curingeffect promoted by biochar additions (by weight of cement: 0, 3,and 7.7%). In fact, compared to the corresponding specimens curedfor the first 48 hours in the formwork, specimens with biochar addition cured directly in air are characterized by a drastically lowerreduction in compressive strength than the reference specimens—that is, approximately 36% and 48%, respectively. This interestingresult can also be exploited in traditional construction techniqueswhere faster demolding is needed.

The production of carbonated aggregates from Class F fly ash(FA) is challenging due to its low calcium content, typically lessthan 10%. This study investigates the production of carbonatedalkali-activated aggregates using FA and calcium carbide sludge(CCS). Sodium hydroxide was used as an activator, and the effectsof autoclave treatment on the properties of these aggregates wereexamined. The optimal mixture, comprising 70% FA and 30%CCS, achieved a single aggregate strength of >5 MPa in autoclavecarbonated (AC) aggregates, comparable to the strength obtainedafter 14 days of water curing without-autoclave carbonated(WAC) aggregates. Both AC and WAC aggregates exhibited a bulkdensity of 790 to 805 kg/m3, and the CO2 uptake was 12.5% and13.3% in AC and WAC aggregates, respectively. Field-emissionscanning electron microscopy (FE-SEM) and Fourier-transforminfrared spectroscopy (FTIR) analysis indicated the formation ofcalcium-aluminum-silicate-hydrate (C-A-S-H) gel in non-carbonatedaggregates, while calcite and vaterite, along with sodiumaluminum-silicate-hydrate (N-A-S-H) gel, formed in carbonatedaggregates. Concrete incorporating AC and WAC aggregatesexhibited compressive strength of 39 and 38 MPa, with concretedensity of 2065 kg/m3 and 2085 kg/m3, respectively. Furthermore,AC and WAC aggregate concrete showed a reduction in CO2emissions of 18% and 31%, respectively, compared to autoclavenon-carbonated (ANC) aggregate concrete. These findings highlightthe potential of producing carbonated alkali-activated aggregatesfrom FA and CCS as sustainable materials for constructionapplications.

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.

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.