As a byproduct of steelmaking, steel slag occupies significant land resources and poses potential environmental and safety challenges due to its extensive accumulation. Recently, steel slag has shown promising applications in the field of concrete. However, considering the complexity of the plateau environment, the utilization of steel slag is relatively lacking, and its low reactivity and poor volume stability remain the main factors restricting its application in plateau concrete. This paper reviews the research status of steel slag activation techniques for concrete, including wet grinding, chemi-excitation, high-temperature activation, and carbonation treatment. The effects of different treatment techniques on the mechanical and durability properties of concrete and the potential issues are discussed. Although different modification methods can improve the activity and volume stability of steel slag to varying degrees, a single modification technology is difficult to achieve the high-quality utilization of steel slag in concrete on the plateau. Based on this, a steel slag grading grinding-magnetic separation utilization technique suitable for high-altitude areas is proposed, which is beneficial for improving the added value and utilization rate of steel slag in concrete.

This study investigates the feasibility of nonlinear coda wave interferometry (NCWI) for evaluating compressive damage in concrete, with a particular focus on the interference caused by the compressive stress-induced slow dynamics. Slow dynamics refers to a phenomenon in which the stiffness of concrete immediately decreases after moderate mechanical conditioning and then logarithmically evolves back to its initial value over time. A series of experiments were conducted to validate this concept. The experimental findings indicate that slow dynamics following the unloading of concrete specimen significantly interfere with NCWI testing. The changes in dv/v caused by the slow dynamics are opposite to those induced by the pump wave in NCWI. After the slow dynamics have been eliminated, an evaluation indicator, defined as the efficient nonlinear level αdv/v, demonstrates an excellent correlation with compressive damage. The value of the indicator decreases with increasing compressive stress. Furthermore, the coda wave interferometry (CWI) and direct wave interferometry (DWI) are performed as comparisons. In summary, the feasibility and superiority of NCWI are demonstrated in concrete compressive damage evaluation.

The microstructure of concrete can be affected by many factors, from non-destructive environmental factors through to destructive damage induced by transient stresses. Coda wave interferometry is a technique that is sensitive enough to detect weak changes within concrete by evaluating the ultrasonic signal perturbation compared to a reference state. As concrete microstructure is sensitive to many factors, it is important to separate their contributions to the observables. In this study, we characterize the relationships between the concrete elastic and inelastic properties, and temperature and relative humidity. We confirm previous theoretical studies that found a linear relationship between temperature changes and velocity variation of the ultrasonic waves for a given concrete mix, and provide scaling factors per Kelvin for multiple settings. We also confirm an anti-correlation with relative humidity using long-term conditioning. Furthermore, we explore beyond the existing studies to establish the relationship linking humidity and temperature changes to ultrasonic wave attenuation.

Concrete compressive strength testing is crucial for construction quality control. The traditional methods are both time-consuming and labor-intensive, while machine learning has been proven effective in predicting the compressive strength of concrete. However, current machine learning-based algorithms lack a thorough comparison among various models, and researchers have yet to identify the optimal predictor for concrete compressive strength. In this study, we developed 12 distinct machine learning-based regressors to conduct a thorough comparison and to identify the optimal model. To study the correlation between compressive strength and various factors, we conducted a comprehensive analysis and selected blast furnace slag, superplasticizer, age, cement, and water as the optimized factor subset. Based on this foundation, grid search and fivefold cross-validation were employed to establish the hyperparameters for each model. The results indicate that the Deepforest-based model demonstrates superior performance compared to the 12 models. For a more comprehensive evaluation of the model\'s performance, we compared its performance with state-of-the-art models using the same independent testing dataset. The results demonstrate that our model achieving the highest performance (R2 of 0.91), indicating its accurate prediction capability for concrete compressive strength.

The industrial production of cement contributes significantly to greenhouse gas emissions, making it crucial to address and reduce these emissions by using fly ash (FA) as a potential replacement. Besides, Graphene oxide (GO) was utilized as nanoparticle in concrete to augment its mechanical characteristics, deformation resistance, and drying shrinkage behaviours. However, the researchers used Response Surface Methodology (RSM) to evaluate the compressive strength (CS), tensile strength (TS), flexural strength (FS), modulus of elasticity (ME), and drying shrinkage (DS) of concrete that was mixed with 5-15% FA at a 5% increment, along with 0.05%, 0.065%, and 0.08% of GO as potential nanomaterials. The concrete samples were prepared by using mix proportions of design targeted CS of about 45 MPa at 28 days. From investigational outcomes, the concrete with 10% FA and 0.05% GO exhibited the greatest CS, TS, FS, and ME values of 62 MPa, 4.96 MPa, 6.82 MPa, and 39.37 GPa, on 28 days correspondingly. Besides, a reduction in the DS of concrete was found as the amounts of FA and GO increased. Moreover, the development and validation of response prediction models were conducted utilizing analysis of variance (ANOVA) at a significance level of 95%. The coefficient of determination (R2) values for the models varied from 94 to 99.90%. Research study indicated that including 10% fly ash (FA) as a substitute for cement, when combined with 0.05% GO, in concrete yields the best results. Therefore, this approach is an excellent option for the building sector.

The degradation of concrete and reinforced concrete structures is a significant technical and economic challenge, requiring continuous repair and rehabilitation throughout their service life. Geopolymers (GPs), known for their high mechanical strength, low shrinkage, and durability, are being increasingly considered as alternatives to traditional repair materials. However, there is currently a lack of understanding regarding the interface bond properties between new geopolymer layers and old concrete substrates. In this paper, using advanced computational techniques, including quantum mechanical calculations and stochastic modeling, we explored the adsorption behavior and interaction mechanism of aluminosilicate oligomers with different Si/Al ratios forming the geopolymer gel structure and calcium silicate hydrate as the substrate at the interface bond region. We analyzed the electron density distributions of the highest occupied and lowest unoccupied molecular orbitals, examined the reactivity indices based on electron density functional theory, performed Mulliken charge population analysis, and evaluated global reactivity descriptors for the considered oligomers. The results elucidate the mechanisms of local and global reactivity of the oligomers, the equilibrium low-energy configurations of the oligomer structures adsorbed on the surface of C-(A)-S-H(I) (100), and their adsorption energies. These findings contribute to a better understanding of the adhesion properties of geopolymers and their potential as effective repair materials.

In this paper, the development of a 3D adaptive probabilistic explicit cracking model for concrete is reported. The contribution offered herein consists in a new adaptive mesh strategy designed to optimize the use of interface elements in probabilistic explicit cracking models. The proposed adaptive mesh procedure is markedly different from other strategies found in the literature, since it takes into account possible influences on the redistribution of stresses after cracking and can also be applied to purely deterministic cracking models. The process of obtaining the most appropriate adaptive mesh procedure involved the development and evaluation of three different adaptivity strategies. Two of these adaptivity strategies were shown to be inappropriate due to issues related to stress redistribution after cracking. The validation results demonstrate that the developed adaptive probabilistic model is capable of predicting the scale effect at a level similar to that experimentally observed, considering the tensile failure of plain concrete specimens. The results also show that different softening levels can be obtained. The proposed adaptive mesh strategy proved to be advantageous, being able to promote significant reductions in the simulation time in comparison with the classical strategy commonly used in probabilistic explicit cracking models.

Super absorbent polymer (SAP) has a capacity to enhance the characteristics of cementitious composites in both their fresh and hardened forms. However, it is essential to recognize that the strength of SAP concrete may decrease. By altering the concrete composition and selecting the appropriate type of SAP, it is possible to reduce this reduction. This work employs machine learning (ML) to tackle the issue of strength degradation. The analysis considers ten distinct variables linked to concrete composition and the type of SAP. The study uses machine learning approaches that involve both regression and classification tasks. The use of ensemble learning greatly improves the quality and accuracy of the results, showing its superiority in combining several models to produce more precise predictions. The findings demonstrate that the Support Vector Machines (SVM) and Extreme Gradient Boosting (XGBoost) regression algorithms accurately forecasted the percentage of reduction in strength in SAP concrete. These predictions were based on the concrete composition and SAP details, resulting in R2 values of 0.90 and 0.88, respectively. Furthermore, XGBoost exhibited the highest accuracy, reaching 0.94, when compared to the various categorization algorithms. According to the results, the mean squared error (MSE) of the ensemble model demonstrated superior outcomes. Furthermore, the SHapley Additive exPlanations (SHAP) reveal that some variables, including SAP%, SAP size, and compressive strength, have a significant influence on the strength reduction model. This study aims to bridge the gap between academic research and practical application by developing a web application that employs ensemble learning to precisely forecast the reduction in compressive strength caused by the usage of SAP.

Recycled aggregate concrete (RAC), mainly made from recycled materials such as construction and demolition waste (CDW), has emerged as a sustainable alternative to natural aggregate concrete (NAC). While RAC offers potential benefits in waste reduction and resource conservation, a comprehensive understanding of its environmental impact and sustainability compared to NAC has been lacking. This study addresses this gap by conducting a thorough review and analysis of comparative Life Cycle Assessment (LCA) studies between RAC and NAC. This paper synthesizes current literature to evaluate the environmental impact of both materials throughout their life cycles, from raw material extraction to disposal. It examines key factors such as energy consumption, greenhouse gas emissions, and resource depletion to provide a thorough comprehension of the effects on the environment of each concrete type throughout their life cycles. Challenges in using RAC as a sustainable concrete option, such as sourcing and quality control, are also discussed, along with recommendations for future research and industry practices. The findings indicate that the environmental impact of RAC compared to NAC is significantly influenced by transport distances and modes. In addition, the choice of functional units in LCAs substantially affects the comparison between RAC and NAC, with strength reliability offering a clear benefit by addressing concrete property variability and better reflecting real-world conditions.

The correct workability of concrete is an essential parameter for its placement and compaction. However, an absence of automatic and transparent measurement methods to estimate the workability of concrete hinders the adaptation from laborious traditional methods such as the slump test. In this paper, we developed a machine-learning framework for estimating the slump class of concrete in the mixer using a stereovision camera. Depth data from five different slump classes was transformed into Haralick texture features to train several machine-learning classifiers. The best-performing classifier achieved a multiclass classification accuracy of 0.8179 with the XGBoost algorithm. Furthermore, we found through statistical analysis that while the denoising of depth data has little effect on the accuracy, the feature extraction of mixer blades and the choice of region of interest significantly increase the accuracy and the efficiency of the classifiers. The proposed framework shows robust results, indicating that stereovision is a competitive solution to estimate the workability of concrete during concrete production.