AI Mix Design of Fly Ash Admixed Concrete Based on Mechanical and Environmental Impact Considerations

It has become very important in the field of concrete technology to develop intelligent models reduce overdependence on laboratory studies prior infrastructure designs. In order achieve this, a database representing global behavior and performance mixes is collected prepared for use. this research work, an extensive literature search was used collect 112 corresponding fly ash binder ratios (FA/B), coarse aggregate (CAg/B), fine (FAg/B), 28-day compressive strength (Fc28), environmental impact point (P) estimated as life cycle assessment greenhouse gas emissions from ash- cement-based concrete. Statistical analysis, linear regression (LNR), artificial intelligence (AI) were conducted database. The material deployed input variables predict Fc28 P response variables. From mix data, it observed that with higher cement content produce strengths carbon footprint compared lower amount FA. results LNR AI modeling showed performed than techniques, R2(SSE) 48.1% (26.5) Fc 91.2% (7.9) P. But ANN, indices 95.5% (9.4) 99.1% (2.6) P, respectively, outclassed EPR 90.3% (13.9) 97.7% (4.2) respectively. Taylor’s variance diagrams also study measured values. show ANN lie within RMSE envelop less 0.5% standard deviation between 15 MPa 20 MPa, while coefficient determination sector lies 95% 99% except LNR, which region 80%. case models, all predicted 1.0%, above, 2.0 3.0 points impact. modeled values shows best distribution, agrees accuracy fits. Lastly, learning ability design tool sustainable based considerations. Doi: 10.28991/CEJ-SP2023-09-03 Full Text: PDF