Sophisticated Ml Techniques for Forecasting Strength in Scc Incorporating Electronic Waste Aggregates

The research examines the outcomes of using electronic waste (E-waste) as an alternate replacement for gravel in SCC (SCC) with 10% of Alccofine and 20% fly ash as supplementary cementitious materials. In total, 13 SCC mixes with E-waste contents from 0 to 30% were investigated to examine their fresh and hardened properties. The experimental outcomes indicates that the slump flow varies from 684.6 mm to 809.2 mm, V-funnel time varies between 6.3 to 10.3 seconds, L- box ratio in the range of 1.023 to 0.787. Composite shows maximum compressive strength of 38.7 MPa, split tensile strength 3.6 MPa, flexural strength 5.4 MPa, modulus elasticity 29,800 MPa, density 2410 kg/m ³ , and bond strength 2407 MPa. From the study it is found that the mechanical performance was best at E-waste replacement 15%, but use of highly E-waste replacing concrete result in low workability and low strength. Mechanical strength of fresh concrete is predicted using four machine learning techniques like Linux Regression, Support Vector Regression, Random Forest, and XGBoost based on fresh concrete properties and mix variables. In particular, XGBoost model provides the best prediction with R ²  > 0.93 and RMS error of prediction is < 0.8 MPa. Six beams (1.5 m × 100 mm × 200 mm) have also been tested under two-point loading to test strength deflection and failure modes. The results show that 15% E-waste has maximum flexural strength of 40.50 MPa, which improves the load capacity by about 12% as compared to 10%. The study shows that moderate quantity (15%) of E-waste addition gives positive total sustainability of SCC effect without any loss in structural integrity.