Sustainable innovation in self-compacted concrete: Integrating by-products and waste rubber for green construction practices

Abstract

Due to increasing concern about global climate change and its negative influence on societies, there is a specific focus on the construction sector as the greatest contributor to greenhouse gas emissions. About 8–10% of global CO2 emissions come from cement production, which releases almost a ton of CO2 for every ton of cement. Growing demand for cement and concrete is attributed to rapid urbanization, industrialization, and economic growth, which also substantially impacts the depletion of natural resources. To address these challenges, a feasible approach is to utilize by-product materials and waste rubber as alternatives to cement and natural fine aggregates in producing self-compacted concrete (SCC). The utilization of various percentages of fly ash (FA), ground granulate blast furnace slag (GGBFS) as cement replacement, and crumb rubber (CR) from waste tires as a partial substitution of fine aggregate offers a suitable choice for the development of sustainable SCC. In addition, compressive strength (CS) is a vital property when considering other characteristics of concrete. Therefore, it is vital to develop reliable models for predicting the CS of SCC to achieve cost, time, and energy savings. Therefore, this research examines the impact of various contents of FA and GGBFS as cement (C) alternatives and CR as a sand replacement on the CS of SCC. To predict the CS of rubberized SCC mixtures, four different models were employed: linear regression (LR), multi-linear regression (MLR), full quadratic (FQ), and M5P-tree. To accomplish this, a comprehensive set of data comprising approximately 436 samples was analyzed to develop the models; as input variables, various mixture proportions, and curing ages were considered. To ensure the accuracy and reliability of the predictive models, several statistical assessments were performed. Based on the statistical assessment tools conducted in this study, the FQ model is considered the most effective model for forecasting the CS of SCC. Based on the statistical assessment tools, the FQ model was also implemented to forecast the splitting tensile and flexural strengths of SCC. The sensitivity analysis indicates that CR and GGBFS content are the best criteria for forecasting the CS of SCC utilizing this data set.