A main global challenge is finding an alternative material for cement, which is a major source of pollution to the environment because it emits greenhouse gases. Investigators play a significant role in global waste disposal by developing appropriate methods for its effective utilization. Geopolymers are one of the best options for reusing all industrial wastes containing aluminosilicate and the best alternative materials for concrete applications. Waste wood ash (WWA) is used with other waste materials in geopolymer production and is found in pulp and paper, wood-burning industrial facilities, and wood-fired plants. On the other hand, the WWA manufacturing industry necessitates the acquisition of large tracts of land in rural areas, while some industries use incinerators to burn wood waste, which contributes to air pollution, a significant environmental problem. This review paper offers a comprehensive review of the current utilization of WWA with the partial replacement with other mineral materials, such as fly ash, as a base for geopolymer concrete and mortar production. A review of the usage of waste wood ash in the construction sector is offered, and development tendencies are assessed about mechanical, durability, and microstructural characteristics. The impacts of waste wood ash as a pozzolanic base for eco-concreting usages are summarized. According to the findings, incorporating WWA into concrete is useful to sustainable progress and waste reduction as the WWA mostly behaves as a filler in filling action and moderate amounts of WWA offer a fairly higher compressive strength to concrete. A detail study on the source of WWA on concrete mineralogy and properties must be performed to fill the potential research gap.

This paper reports the results of a study conducted to examine the impacts of adding graphene oxide (GO) to GBFS-fly ash-based geopolymer concrete. The geopolymer concrete’s compressive strength, thermal conductivity, and modulus of elasticity were assessed. X-ray diffraction (XRD) analysis was conducted to understand the differences in mineralogical composition and a rapid chloride penetration test (RCPT) to investigate the changes in the permeability of chloride ions imposed by GO addition. The results showed that adding 0.25 wt.% GO increases the modulus of elasticity and compressive strength of GBFS-FA concrete by 30.5% and 37.5%, respectively. In contrast, permeability to chloride ions was reduced by 35.3% relative to the GO-free counterparts. Thermal conductivity was decreased as GO dosage increased, with a maximum reduction of 33% being observed in FA65-G35 wt.% samples. Additionally, XRD showed the suitability of graphene oxide in geopolymer concrete. The present research demonstrates very promising features of GO-modified concrete that exhibit improved strength development and durability compared to traditional concrete, thus further advocating for the wider utilization of geopolymer concrete manufactured from industrial byproducts.

The current practice of concrete is thought to be unsuitable because it consumes large amounts of cement, sand, and aggregate, which causes depletion of natural resources. In this study, a step towards sustainable concrete was made by utilizing recycled concrete aggregate (RCA) as a coarse aggregate. However, researchers show that RCA causes a decrease in the performance of concrete due to porous nature. In this study, waste glass (WG) was used as a filler material that filled the voids between RCA to offset its negative impact on concrete performance. The substitution ratio of WG was 10, 20, or 30% by weight of cement, and RCA was 20, 40, and 60% by weight of coarse aggregate. The slump cone test was used to assess the fresh property, while compressive, split tensile, and punching strength were used to assess the mechanical performance. Test results indicated that the workability of concrete decreased with substitution of WG and RCA while mechanical performance improved up to a certain limit and then decreased due to lack of workability. Furthermore, a statical tool response surface methodology was used to predict various strength properties and optimization of RCA and WG.

Several types of research currently use machine learning (ML) methods to estimate the mechanical characteristics of concrete. This study aimed to compare the capacities of four ML methods: eXtreme gradient boosting (XG Boost), gradient boosting (GB), Cat boosting (CB), and extra trees regressor (ETR), to predict the splitting tensile strength of 28-day-old self-compacting concrete (SCC) made from recycled aggregates (RA), using data obtained from the literature. A database of 381 samples from literature published in scientific journals was used to develop the models. The samples were randomly divided into three sets: training, validation, and test, with each having 267 (70%), 57 (15%), and 57 (15%) samples, respectively. The coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) metrics were used to evaluate the models. For the training data set, the results showed that all four models could predict the splitting tensile strength of SCC made with RA because the R² values for each model had significance higher than 0.75. XG Boost was the model with the best performance, showing the highest R² value of R² = 0.8423, as well as the lowest values of RMSE (=0.0581) and MAE (=0.0443), when compared with the GB, CB, and ETR models. Therefore, XG Boost was considered the best model for predicting the splitting tensile strength of 28-day-old SCC made with RA. Sensitivity analysis revealed that the variable contributing the most to the split tensile strength of this material after 28 days was cement.

Recycled aggregate is a good option to be used in concrete production as a coarse aggregate that results in environmental benefits as well as sustainable development. However, recycled aggregate causes a reduction in the mechanical and durability performance of concrete. On the other hand, the removal of industrial waste would be considerably decreased if it could be incorporated into concrete production. One of these possibilities is the substitution of the cement by slag, which enhances the concrete poor properties of recycled aggregate concrete as well as provides a decrease in cement consumption, reducing carbon dioxide production, while resolving a waste management challenge. Furthermore, steel fiber was also added to enhance the tensile capacity of recycled aggregate concrete. The main goal of this study was to investigate the characteristics of concrete using ground granulated blast-furnace slag (GGBS) as a binding material on recycled aggregate fibers reinforced concrete (RAFRC). Mechanical performance was assessed through compressive strength and split tensile strength, while durability aspects were studied through water absorption, acid resistance, and dry shrinkage. The results detected from the different experiments depict that, at an optimum dose (40% RCA, 20%GGBS, and 2.0%), compressive and split tensile strength were 39% and 120% more than the reference concrete, respectively. Furthermore, acid resistance at the optimum dose was 36% more than the reference concrete. Furthermore, decreased water absorption and dry shrinkage cracks were observed with the substitution of GGBS into RAFRC.

A considerable amount of discarded building materials are produced each year worldwide, resulting in ecosystem degradation. Self-compacting concrete (SCC) has 60–70% coarse and fine particles in its composition, so replacing this material with another waste material, such as recycled aggregate (RA), reduces the cost of SCC. This study compares novel Artificial Neural Network algorithm techniques—Levenberg–Marquardt (LM), Bayesian regularization (BR), and Scaled Conjugate Gradient Backpropagation (SCGB)—to estimate the 28-day compressive strength (f’c) of SCC with RA. A total of 515 samples were collected from various published papers, randomly splitting into training, validation, and testing with percentages of 70, 10 and 20. Two statistical indicators, correlation coefficient (R) and mean squared error (MSE), were used to assess the models; the greater the R and lower the MSE, the more accurate the algorithm. The findings demonstrate the higher accuracy of the three models. The best result is achieved by BR (R = 0.91 and MSE = 43.755), while the accuracy of LM is nearly the same (R = 0.90 and MSE = 48.14). LM processes the network in a much shorter time than BR. As a result, LM and BR are the best models in forecasting the 28 days f’c of SCC having RA. The sensitivity analysis showed that cement (28.39%) and water (23.47%) are the most critical variables for predicting the 28-day compressive strength of SCC with RA, while coarse aggregate contributes the least (9.23%).