The depletion of natural sand resources and the environmental impact of cement production necessitate sustainable alternatives in concrete manufacturing. This study evaluates the potential of zeolite, ground granulated blast-furnace slag (GGBS), and construction and demolition waste (CDW) as partial replacements for sand in concrete mix proportions. Experimental investigations revealed that the optimal mix proportion, identified as Mix Batch M4 (60% Sand, 20% Zeolite, 10% GGBS, and 10% CDW), achieved a compressive strength (CS) of 67.37 MPa, flexural strength (FS) of 6.80 MPa, split tensile strength (ST) of 5.61 MPa, and notable reductions in water absorption (WA) to 4.00% and drying shrinkage (DS) to 4.02%. Additionally, durability improvements included a 30% reduction in rapid chloride permeability and enhanced ultrasonic pulse velocity (UPV) and rebound hammer (RH) values. Advanced machine learning models were utilized to analyze and optimize the mix designs, integrating the Sparrow Search Algorithm (SSA) with models such as Extreme Gradient Boosting (XGB), Backpropagation Neural Network (BPNN), Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and LightGBM. The XGB model demonstrated the highest predictive accuracy with an R² of 1.000. Multi-objective optimization techniques, including Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Genetic Algorithm with Fuzzy models, were employed to refine mix proportions further, balancing mechanical properties, material sustainability, and environmental benefits. This study highlights significant reductions in natural sand consumption and waste generation while enhancing concrete performance. Practical implications include reduced environmental impact, improved resource efficiency, and the promotion of circular economy principles. These findings provide a pathway toward innovative and sustainable concrete solutions, aligning with global sustainability goals in the construction industry.

The Ganga, a critical lifeline for nearly 400 million people, is increasingly threatened by heavy metal contamination from industrial effluents, agricultural runoff, and urban discharge. This study quantified eight heavy metals—iron (Fe), manganese (Mn), zinc (Zn), chromium (Cr), lead (Pb), nickel (Ni), copper (Cu), and cadmium (Cd)—in suspended solid particles collected from ten sites along a 210-kilometer stretch between Kanpur and Prayagraj, Uttar Pradesh, in July 2024. Suspended particulates were isolated by membrane filtration and analyzed using Atomic Absorption Spectrophotometry (AAS). Median concentrations (μg/g) were Fe (47,246), Mn (940), Zn (206), Cr (122), Pb (65.5), Ni (64.8), Cu (61.3), and Cd (1.61), with the highest values for Fe, Mn, Zn, Pb, and Cu observed at Nawabganj and Manikpur. Enrichment Factor (EF) analysis, using Fe as the reference element, indicated minor anthropogenic enrichment for Pb (1.32), Cd (1.27), Zn (1.20), Cr (1.13), and Ni (1.07), particularly at Dalmau Bridge, Unchahar, Nawabganj, and Manikpur. Mn and Cu showed EF values below 1, suggesting predominantly natural origins, although strong inter-metal correlations point to potential industrial sources for Cu. Multivariate statistical tools (PCA, clustering) revealed spatial contamination patterns and identified Nawabganj and Manikpur as consistent pollution hotspots. Compared to global rivers, the Ganga exhibited significantly higher suspended solid heavy metal loads (e.g., Pb: 65.5 μg g ⁻¹ versus Yangtze: 43 μg g ⁻¹ ). These findings underscore the urgent need for site-specific pollution control, stricter discharge regulations, and long-term ecological monitoring. This study provides a replicable framework for riverine metal pollution assessment and supports sustainable management initiatives like the Namami Gange program.