This research presents a comprehensive experimental study on the compressive strength and durability performance of environmentally sustainable concrete incorporating red mud (RM) and recycled concrete aggregate (RCA). Throughout all mix designs, the quantities of fine aggregate, silica fume, basalt fiber and water/binder ratios were kept constant, while the cement and coarse aggregate contents were systematically replaced with red mud (0–15%) and recycled concrete aggregates (0–100%) respectively. The experimental results revealed that increasing red mud content led to a consistent increase in compressive strength up to 10% replacement of cement with RM and then showed the decline, with the increment of 5.46% for 0% RA, 12.77% for 50% RA, 18.45% for 75% RA and 19.14% for 100% RA corresponding to the 10% red mud replacement for 28 days curing. Similarly, replacing NCA with RCA caused strength reductions of 22.52% for 50% RA, 38.96% for RA 75% and 57.07% for 100% at 10% replacement of cement with red mud and 28 days of curing, respectively. A similar trend is seen in acid resistance of red mud-based RAC and this reduction reaches 65.1% for 0% RM, 61.7% for 5% RM, 59.44% for 10% RM and 60.62% for 15% RM when recycled aggregates replacement reached from 0 to 100% respectively. However, acid resistance of concrete increased from 0 to 10% inclusion of red mud and then decreased at 15% replacement. In the second phase of the study, the potential of machine learning (ML) algorithms to predict the compressive strength of concrete mixes containing RM and RCA was evaluated. A total of four ensemble-based ML models including random forest (RF), KNN, AdaBoost and XGBoost were trained and tested using 251 datasets for RM and RCA mixes, respectively. Among the models, XGBoost exhibited the highest accuracy and reliability in predicting compressive strength. The study also included an analysis of the influence of individual input parameters on the model’s predictive outcomes using SHAP analysis and found the curing age is the most influential parameter while RCA, SF and NCA are the following one.

Hilly terrain has notable challenges of flash flooding, requiring a cost-effective and sustainable management approach. Nature-based solutions (NBS) provide significant sustainability, however need methodical assessment under diverse hydrological conditions. To fill this gap, the current investigation explores the significance of the NBS utilizing artificial intelligence (AI) techniques for optimizing flood resilience through the prediction of peak discharge generated from the hilly terrain. For this purpose, two AI advanced models including random forest (RF) and support vector regression (SVR) model were utilized for the assessment of laboratory scale data series of slope, rainfall intensity (P), and time ratio (T/Tc, T: total time, Tc: time of concentration) with flexible (FV) and rigid vegetation (RV). A total of 344 data series were collected, split into three different phases of training (70%), testing (15%), and validation (15%) for both AI models. The values of root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE), along with SHAP (SHapley Additive exPlanations) analysis, are used to assess the performance of AI models. Further Monte Carlo simulation was performed to clarify uncertainty and feature importance. The result demonstrated 8% more reduction of the peak discharge in the case of flexible vegetation, because of its surface resistance and infiltration capability. The RF model has greater prediction power compared to the SVR model Due to a higher R-value of 0.9809 for FV and 0.9906 for RV conditions. The result of SHAP analysis demonstrates greater influence of time ratio on peak discharge under FV (SHAP range:±25 and RV (SHAP range: ±30), while the moderate impact of rainfall intensity was observed in the case of FV (SHAP range: ±5) and RV (SHAP range: ±7). The findings concluded that AI-driven models utilized for NBS enhance resilience against flooding in hilly terrain. Urban planners and policymakers should utilize an AI-driven approach for nonlinear hydrological phenomena.

This study aimed to develop a novel composite adsorbent combining peat (PS), limestone (LS), zeolite (ZEO), and activated carbon (AC), with ordinary Portland cement (OPC) as a binder (40% by weight), for the simultaneous removal of ammoniacal nitrogen (NH₃-N) and chemical oxygen demand (COD) from stabilized landfill leachate. The composite was characterized using X-ray fluorescence (XRF), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), Brunauer–Emmett–Teller (BET) analysis, and pH at zero-point charge (pHzpc). The material exhibited high SiO₂ and CaO content, functional groups (e.g., Si–O-Si, N–H, O–H, C–O, C–N, and O–C.), a rough and heterogeneous surface morphology, a surface area of 105.96 m²/g, and a pHzpc of 11.25. Batch experiments determined optimal adsorption conditions: 200 rpm shaking speed, 120-min contact time, pH 7, particle size of 2.36–3.35 mm, and dosage of 57 g/L. The Langmuir isotherm model provided the best fit for NH₃-N and COD adsorption with adsorption capacities of 26.18 mg/g and 47.39 mg/g, respectively (R² = 0.9941 and 0.9814). Kinetic studies indicated pseudo-second-order kinetics, suggesting chemisorption as the rate-limiting process. These findings demonstrate the composite (PS, LS, ZEO, and AC) potential as an efficient and sustainable adsorbent for treating stabilized landfill leachate. Further studies should focus on evaluating the performance of composite adsorbents for the removal of NH₃-N, COD, or other pollutants from mining industry, domestic, or combined effluents, as well as their potential application in air pollution control.

The aim of this study was to optimize several parameters that affect the Electrocoagulation (EC) process of Cadmium (Cd) from polluted wastewater. To estimate the contribution of each parameter to the overall removal process, an experimental design methodology was employed. System performance was optimized using Response Surface Methodology (RSM), focusing on key factors, such as pH, amperage, and salt (NaCl) concentration. Minitab 19 software was utilized to develop a full model equation, including the main effects and interactions between the variables. The analysis revealed that pH and amperage were the most influential factors, contributing 43.04% and 26.27%, respectively, to system efficiency. The optimal parameter range for achieving a yield (above 95%) was identified through response contour plots and interaction analyses: pH ranged between 6.9 and 11, amperage between 300 and 500 mA, and salt concentration between 0.1 and 0.6 g/L. Further refinement of these ranges was visualized using response surface diagrams and the Box-Behnken design. Practical experiments were conducted using the optimized values (pH = 6.2, amperage = 280 mA, salt = 0.46 g/L), and the results were compared to the theoretical model. A minimal error margin of less than 2% between the practical and statistical results validated the model's accuracy.