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Rapid urbanization and industrialization with corresponding economic growth have increased concrete production, leading to resource depletion and environmental pollution. The mentioned problems can be resolved by using recycled aggregates and industrial waste ashes as natural aggregate and cement replacement in concrete production. Incorporating different by-product ashes and recycled plastic (RP) aggregates are viable options to produce sustainable self-compacting concrete (SCC). On the other hand, compressive strength is an essential characteristic among other evaluated properties. As a result, establishing trustworthy models to forecast the compressive strength of SCC is critical to saving cost, time, and energy. Furthermore, it provides valuable instruction for planning building projects and determining the best time to remove the formwork. In this study, four alternative models were suggested to predict the compressive strength of SCC mixes produced by RP aggregates: the artificial neural network (ANN), nonlinear model, linear relationship model, and multi-logistic model. To do so, an extensive set of data consisting of 400 mixtures were extracted and analyzed to develop the models, various mixture proportions and curing times were considered as input variables. To test the effectiveness of the suggested models, several statistical evaluations, including coefficient of determination ( R  2 ), scatter index, root mean squared error (RMSE), mean absolute error (MAE), and Objective (OBJ) value were utilized. Compared to other models, the ANN model performed better to forecast the compressive strength of SCC mixes incorporating RP aggregates. The RMSE, MAE, OBJ, and R  2 values for this model were 5.46 MPa, 2.31 MPa, 4.26 MPa, and 0.973, respectively.

clean technologies and environmental policy

Soft computing techniques to predict the compressive strength of green self-compacting concrete incorporating recycled plastic aggregates and industrial waste ashes