Strength and Acid Resistance of Ceramic-Based Self-Compacting Alkali-Activated Concrete: Optimizing and Predicting Assessment

Intelligent optimization for modelling superhydrophobic ceramic membrane oil flux and oil-water separation efficiency: Evidence from wastewater treatment and experimental laboratory

Due to the significant energy and economic losses brought on by the global oil spill, there has been an increased interest in oil-water separation. This study presents strong non-linear machine learning models (support vector regression (SVR) and Gaussian process regression (GPR)) with the Response surface method (RSM) to predict the oil flux and oil-water separation efficiency of wastewater using ceramic membrane technology. For the model development and prediction of oil flux (OF) and oil-water separation efficiency (OSE), oil concentration (mg/L), feed flow rate (mL/min), and pH were considered as input variables. The input variables are combined in three combinations to study the most contributing input features to the models' performance. Mean square error (MSE) and Nash-Sutcliffe coefficient efficiency (NSE) were used to assess the prediction performances of the developed models with the different number of input combinations considered in the study. For the two target variables (OF and OSE), GPR and SVR models were used to separately predict them. For OF, the SVR-2 [Combo-2] model (MSE = 0.9255 and NSE = 2.7976) performed better with higher prediction accuracy compared to GPR-2 [Combo-2] model (MSE = 0.763 and NSE = 6.437). In addition, for OSE, the GPR-3 [Combo-3] model (MSE = 0.995 and NSE = 0.5544) performed slightly better than SVR-3 [Combo-3] model (MSE = 0.992 and NSE = 0.8066). The results showed that the SVR model with the combo-2 and GPR-3 models for OF and OSE variables are the proposed models with the best performance and accuracy. This machine learning study will aid in better evaluating the function of materials such as ceramic in membrane performance features such as oil flux and rejection prediction, separation efficiency, water recovery, membrane fouling, and so on. As for academics and manufacturers, this machine learning (ML) strategy will boost performance and allow a better understanding of system governance.

Flexural Behavior of Reinforced Concrete Beams under Instantaneous Loading: Effects of Recycled Ceramic as Cement and Aggregates Replacement

The flexural behavior of five reinforced concrete beams containing recycled ceramic as cement and aggregate replacement subjected to a monotonic static load up to failure was studied. A full-scale, four-point load test was conducted on these beams for 28 days. The experimental results were compared with the conventional concrete as a control specimen. The cross-section and effective span of these beams were (160 × 200 mm) and 2200 mm, respectively. The data recorded during the tests were the ultimate load at failure, steel-reinforcement bar strain, the strain of concrete, cracking history, and mode of failure. The beam containing 100% recycled aggregates displayed an ultimate load of up to 99% of the control beam specimen. In addition, the first crack load was almost similar for both specimens (about 14 kN). The deflection of the beam composed of 100% of the recycled aggregates was reduced by 43% compared to the control specimen. Regardless of the recycled ceramic aggregates ratio, quantities such as service, yield, and ultimate load of the proposed beams exhibited a comparable trend. It was asserted that the ceramic wastes might be of potential use in producing high-performance concrete needed by the structural industry. It might be an effective strategy to decrease the pressure on the environment, thus reducing the amount of natural resources usage.