The use of date palm fiber (DPF) as natural fiber in concrete and mortar continues to gain acceptability due to its low-cost and availability. However, the main disadvantage of DPF in cement-based composites is that it reduces compressive strength and increases the porosity of the composite. Hence, for DPF to be efficiently used in concrete, its negative effects must be counteracted. Therefore, in this study, silica fume was employed as supplementary cementitious material to alleviate the negative effects of DPF on the strength and porosity of concrete. The DPF was added in different dosages of 0%, 1%, 2%, and 3% by weight of binder materials. Silica fume was used as a cement replacement material at dosages of 0% to 15% (intervals of 5%) by volume of cement. The unit weights, mechanical strengths, water absorption, and microstructural morphology were all evaluated. The concrete’s fresh and hardened densities were reduced with the increment in DPF and silica fume. The compressive strength declined at all ages with the increment in DPF addition, while the flexural and splitting tensile strengths improved with addition of up to 2% DPF. Furthermore, the concrete’s water absorption escalated with an increase in DPF content. Silica fume significantly enhanced the mechanical strength of the concrete. The dissipation in compressive strength with the addition of up to 2% DPF was mitigated by replacing up to 10% cement with silica fume, where it densified the microstructure and refined the interfacial transition zone between the fibers and cement matrix, hence significantly decreasing the porosity and enhancing durability.

Recycled concrete aggregate (RCA) collected from the demolition of old reinforced concrete structures can be reused to prepare structural and non-structural concrete, thereby protecting the environment by preserving natural resources. This study explores RCA’s use, collected from the crushed concrete of different building projects in Riyadh, to manufacture fresh self-compacting concrete (SCC) and investigate its properties in the fresh and hardened state. Four SCC mixes were prepared by replacing natural aggregate (NA) with RCA at 0%, 25%, 50%, and 75% replacement levels. The water-cement (w/c) ratio was maintained constant at 0.38 for all the mixes. Slump Flow, J-ring, and V-funnel tests were performed on the SCC mixes in the fresh state, and the compressive strength of hardened concrete was determined after seven, 14, and 28 days. Water absorption and split tensile tests were also carried out for all the mixes. The findings revealed that it is possible to reach compressive strengths higher than 40 MPa at 28 days for RCA replacement level of 75% by using a superplasticizer and low w/c ratio. The decrease in compressive strength concerning the SCC-NA mix was 25% for 75% replacement level. The highest split tensile strength at 28 days was around 3.3 MPa for a 50% replacement level. The lowest water absorption was 3.2% for SCC-NA, which was gradually increased and was highest at 5.6% for 75% replacement level.

The purpose of the research is to predict the compressive and flexural strengths of the concrete mix by using waste marble powder as a partial replacement of cement and sand, based on the experimental data that was acquired from the laboratory tests. In order to accomplish the goal, the models of Support vector machines, Support vector machines with bagging and Stochastic, Linear regression, and Gaussian processes were applied to the experimental data for predicting the compressive and flexural strength of concrete. The effectiveness of models was also evaluated by using statistical criteria. Therefore, it can be inferred that the gaussian process and support vector machine methods can be used to predict the respective outputs, i.e., flexural and compressive strength. The Gaussian process and Support vector machines Stochastic predicts better outcomes for flexural and compressive strength because it has a higher coefficient of correlation (0.8235 and 0.9462), lower mean absolute and root mean squared error values as (2.2808 and 1.8104) and (2.8527 and 2.3430), respectively. Results suggest that all applied techniques are reliable for predicting the compressive and flexural strength of concrete and are able to reduce the experimental work time. In comparison to input factors for this data set, the number of curing days followed by the CA, C, FA, w, and MP is essential in predicting the flexural and compressive strength of a concrete mix for this data set.

Greywater is the most sustainable option to address the growing need for fresh water. This study aimed to identify the optimal operation variables of an electro-coagulation filtration (ECF) system for treating domestic greywater, using different conditions (e.g., different electrode combinations (Al-Fe-Al-Fe), initial pH (6.8–8.4), operating time (10–60 min), and voltage (6–24 volts)). A statistical data analysis was performed to evaluate the experimental conditions for modeling the chemical oxygen demand (COD), the total dissolved solids (TDSs), turbidity, and chloride removal effectiveness, almost ranging from (85 to 94%), respectively, with energy consumption using the response surface methodology (RSM) and the ANOVA test. When comparing the experimental and predicted model values, it was proved that the model fairly describes the experimental values with the R² values determined >0.99 for COD, TDSs, turbidity, chloride, and energy consumption, suggesting a regression sustainability of the model. The sludge properties were characterized using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and FTIR spectroscopy, which indicated the removal of organic matter during the ECF, similar in composition, independently of the different applied voltage values used. The results of this study suggest the ECF significantly reduces the pollutants load in greywater, showing the aluminum-iron-based electrodes as a viable option to treat greywater with optimal operational costs ranging from (0.12 to 0.4) US$ m⁻³ under different voltage conditions and parameters. This study establishes a path for greywater treatment technology that is economical and environmentally responsible for wastewater management that leads to sustainability.

The study’s goal is to assess the flexural strength of concrete that includes waste marble powder using machine learning methods, i.e., ANFIS, Support vector machines, and Gaussian processes approaches. Flexural strength has also been studied by using the most reliable approach of sensitivity analysis in order to determine the influential independent variable to predict the dependent variable. The entire dataset consists of 202 observations, of which 120 were experimental and 82 were readings from previous research projects. The dataset was then arbitrarily split into two subsets, referred to as the training dataset and the testing dataset, each of which contained a weighted percentage of the total observations (70–30). Output was concrete mix flexural strength, whereas inputs comprised cement, fine and coarse aggregates, water, waste marble powder, and curing days. Using statistical criteria, an evaluation of the efficacy of the approaches was carried out. In comparison to other algorithms, the results demonstrate that the Gaussian process technique has a lower error bandwidth, which contributes to its superior performance. The Gaussian process is capable of producing more accurate predictions of the results of an experiment due to the fact that it has a higher coefficient of correlation (0.7476), a lower mean absolute error value (1.0884), and a smaller root mean square error value (1.5621). The number of curing days was identified as a significant predictor, in addition to a number of other factors, by sensitivity analysis.

Pavement design is a long-term structural analysis that is required to distribute traffic loads throughout all road levels. To construct roads for rising traffic volumes while preserving natural resources and materials, a better knowledge of road paving materials is required. The current study focused on the prediction of Marshall stability of asphalt mixes constituted of glass, carbon, and glass-carbon combination fibers to exploit the best potential of the hybrid asphalt mix by applying five machine learning models, i.e., artificial neural networks, Gaussian processes, M5P, random tree, and multiple linear regression model and further determined the optimum model suitable for prediction of the Marshall stability in hybrid asphalt mixes. It was equally important to determine the suitability of each mix for flexible pavements. Five types of asphalt mixes, i.e., glass fiber asphalt mix, carbon fiber asphalt mix, and three modified asphalt mixes of glass-carbon fiber combination in the proportions of 75:25, 50:50, and 25:75 were utilized in the investigation. To measure the efficiency of the applied models, five statistical indices, i.e., coefficient of correlation, mean absolute error, root mean square error, relative absolute error, and root relative squared error were used in machine learning models. The results indicated that the artificial neural network outperformed other models in predicting the Marshall stability of modified asphalt mix with a higher value of the coefficient of correlation (0.8392), R² (0.7042), a lower mean absolute error value (1.4996), and root mean square error value (1.8315) in the testing stage with small error band and provided the best optimal fit. Results of the feature importance analysis showed that the first five input variables, i.e., carbon fiber diameter, bitumen content, hybrid asphalt mix of glass-carbon fiber at 75:25 percent, carbon fiber content, and hybrid asphalt mix of glass-carbon fiber at 50:50 percent, are highly sensitive parameters which influence the Marshall strength of the modified asphalt mixes to a greater extent.