The application of multiphysics models and soft computing techniques is gaining enormous attention in the construction sector due to the development of various types of concrete. In this research, an improved form of supervised machine learning, i.e., multigene expression programming (MEP), has been used to propose models for the compressive strength (fc'), splitting tensile strength (fSTS), and flexural strength (fFS) of sustainable bagasse ash concrete (BAC). The training and testing of the proposed models have been accomplished by developing a reliable and comprehensive database from published literature. Concrete specimens with varying proportions of sugarcane bagasse ash (BA), as a partial replacement of cement, were prepared, and the developed models were validated by utilizing the results obtained from the tested BAC. Different statistical tests evaluated the accurateness of the models, and the results were cross-validated employing a k-fold algorithm. The modeling results achieve correlation coefficient (R) and Nash-Sutcliffe efficiency (NSE) above 0.8 each with relative root mean squared error (RRMSE) and objective function (OF) less than 10 and 0.2, respectively. The MEP model leads in providing reliable mathematical expression for the estimation of fc', fSTS and fFS of BA concrete, which can reduce the experimental workload in assessing the strength properties. The study's findings indicated that MEP-based modeling integrated with experimental testing of BA concrete and further cross-validation is effective in predicting the strength parameters of BA concrete.
The purpose of this research is to study the effects of quarry rock dust (QRD) and steel fibers (SF) inclusion on the fresh, mechanical, and microstructural properties of fly ash (FA) and ground granulated blast furnace slag (SG)-based geopolymer concrete (GPC) exposed to elevated temperatures. Such types of ternary mixes were prepared by blending waste materials from different industries, including QRD, SG, and FA, with alkaline activator solutions. The multiphysical models show that the inclusion of steel fibers and binders can enhance the mechanical properties of GPC. In this study, a total of 18 different mix proportions were designed with different proportions of QRD (0%, 5%, 10%, 15%, and 20%) and steel fibers (0.75% and 1.5%). The slag was replaced by different proportions of QRD in fly ash, and SG-based GPC mixes to study the effect of QRD incorporation. The mechanical properties of specimens, i.e., compressive strength, splitting tensile strength, and flexural strength, were determined by testing cubes, cylinders, and prisms, respectively, at different ages (7, 28, and 56 days). The specimens were also heated up to 800 °C to evaluate the resistance of specimens to elevated temperature in terms of residual compressive strength and weight loss. The test results showed that the mechanical strength of GPC mixes (without steel fibers) increased by 6-11%, with an increase in QRD content up to 15% at the age of 28 days. In contrast, more than 15% of QRD contents resulted in decreasing the mechanical strength properties. Incorporating steel fibers in a fraction of 0.75% by volume increased the compressive, tensile, and flexural strength of GPC mixes by 15%, 23%, and 34%, respectively. However, further addition of steel fibers at 1.5% by volume lowered the mechanical strength properties. The optimal mixture of QRD incorporated FA-SG-based GPC (QFS-GPC) was observed with 15% QRD and 0.75% steel fibers contents considering the performance in workability and mechanical properties. The results also showed that under elevated temperatures up to 800 °C, the weight loss of QFS-GPC specimens persistently increased with a consistent decrease in the residual compressive strength for increasing QRD content and temperature. Furthermore, the microstructure characterization of QRD blended GPC mixes were also carried out by performing scanning electron microscopy (SEM), X-ray diffraction (XRD), and energy dispersive spectroscopy (EDS).