ABSTRACT
Tire rubber waste is globally accumulated every year. Therefore, a solution to this problem should be found since, if landfilled, it is not biodegradable and causes environmental issues. One of the most effective ways is recycling those wastes or using them as a replacement for normal aggregate in the concrete mixture, which has high impact resistance and toughness; thus, it will be a good choice. In this study, 135 data were collected from previous literature to develop a model for the prediction of rubberized concrete compressive strength; the database comprised different mixture proportions, the maximum size of the rubber (1-40 mm), and the rubber percentage (0-100%) replacing natural fine and coarse aggregates were among the input parameters in addition to cement content (380-500 kg/m3) water content (129-228 kg/m3), fine aggregate content (0-925 kg/m3), coarse aggregate content (0-1303 kg/m3), and curing time of the samples (1-96 Days); then the collected data were used in developing Multi Expression Programming (MEP), Artificial Neural Network (ANN), Multi Adaptive Regression Spline (MARS), and Nonlinear Regression (NLR) Models for predicting compressive strength (CS) of rubberized concrete. The parametric analysis reveals that as the maximum rubber size increases, the reduction in compressive strength becomes more pronounced. Notably, this strength decline is more significant when rubber replaces coarse aggregate than its replacement of fine aggregate. Among the input parameters considered, it is evident that the fine aggregate content exerts the most substantial influence on the compressive strength of rubberized concrete. Its impact on predicting compressive strength surpasses other factors, with the concrete samples' curing time ranking second in importance. According to the assessment tools, the ANN model performed better than other developed models, with high R2 and lower RMSE, MAE, SI, and MAPE. Additionally, ANN and MARS models predicted the CS of different sizes better than MEP and NLR models. Subsequently, we employed the collected data to develop predictive models using Multi Expression Programming (MEP), Artificial Neural Network (ANN), Multi Adaptive Regression Spline (MARS), and Nonlinear Regression (NLR) techniques to forecast the compressive strength (CS) of rubberized concrete. The statistical analysis tools assessed the performance of these developed models through various evaluation criteria, including the Coefficient of Determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), and Mean Absolute Percentage Error (MAPE). In summary, our study underscores the efficacy of recycling rubber materials in concrete production. It presents a powerful predictive model for assessing the compressive strength of rubberized concrete, with the ANN model standing out as the most accurate and reliable choice for this purpose.
ABSTRACT
In recent years, geopolymer has been developed as an alternative to Portland cement (PC) because of the significant carbon dioxide emissions produced by the cement manufacturing industry. A wide range of source binder materials has been used to prepare geopolymers; however, fly ash (FA) is the most used binder material for creating geopolymer concrete due to its low cost, wide availability, and increased potential for geopolymer preparation. In this paper, 247 experimental datasets were obtained from the literature to develop multiscale models to predict fly-ash-based geopolymer mortar compressive strength (CS). In the modeling process, thirteen different input model parameters were considered to estimate the CS of fly-ash-based geopolymer mortar. The collected data contained various mix proportions and different curing ages (1 to 28 days), as well as different curing temperatures. The CS of all types of cementitious composites, including geopolymer mortars, is one of the most important properties; thus, developing a credible model for forecasting CS has become a priority. Therefore, in this study, three different models, namely, linear regression (LR), multinominal logistic regression (MLR), and nonlinear regression (NLR) were developed to predict the CS of geopolymer mortar. The proposed models were then evaluated using different statistical assessments, including the coefficient of determination (R2), root mean squared error (RMSE), scatter index (SI), objective function value (OBJ), and mean absolute error (MAE). It was found that the NLR model performed better than the LR and MLR models. For the NLR model, R2, RMSE, SI, and OBJ were 0.933, 4.294 MPa, 0.138, 4.209, respectively. The SI value of NLR was 44 and 41% lower than the LR and MLR models' SI values, respectively. From the sensitivity analysis result, the most effective parameters for predicting CS of geopolymer mortar were the SiO2 percentage of the FA and the alkaline liquid-to-binder ratio of the mixture.
