One of the most fundamental developments in improving the mechanical properties of concrete is the introduction of recycled coarse aggregate, which offers an environmentally preferable substitute for traditional waste management techniques. Using recycled coarse aggregate and a small number of mix proportions for the concrete components, a few studies looked at the mechanical properties of concrete. To assess the impact of recycled coarse aggregate on the long-term compressive strength of concrete at various mix proportions and different compressive strength ranges, this study analyzed four models, including linear regression (LR), nonlinear regression (NLR), pure quadratic (PQ), and full quadratic (FQ). Three datasets training, testing, and validating, each containing 314 data points culled from various studies, were used to apply the models. The recycled coarse aggregate (RA) density ranged from 0 to 1240 kg/m3, and the curing time (t) varied from 1 to 90 days. While the predicted compressive strength of the models ranged between 5 and 75 MPa, the compressive strength of the data gathered from the experimental work of several studies ranged from 8 to 78 MPa. The models’ accuracy was assessed using several metrics, including the coefficient of determination (R2), the root-mean-square error (RMSE), the scatter index (SI), the objective (OBJ), and the mean absolute error (MAE).
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Using two different test standards (ASTM and BS), the influence of five different sizes of sand on the ultimate stress (MPa) of hand-mixed cement-grouted sands modified with polymer is discussed in this study. The characteristics of cement-grouted sands modified with polymer up to 0.16% (percent weight of dry cement) were evaluated and measured in fresh and hardened conditions. Adding polymer decreased the water/cement ratio (w/c) from 0.6 to 0.5, and it kept the flow time of the cement-based grout in the range of 18 to 23 s recommended by ASTM standard. Using mix proportion and curing time, adding polymer significantly increased the prismatic and cylindrical compressive strength (MPa) by 113 to 577% and 53 to 459%. Several mathematical approaches such as linear regression (LR), Nonlinear regression (NLR), multilinear regression (MLR), Artificial neural network (ANN), and M5P-tree were used to predict the compression strength of cement-grouted sand with a different size of sand, w/c, polymer content, and curing age. Based on the scatter index (SI), objective function (OBJ) assessments, and training and testing datasets, the compressive strength of the cement-grouted sands can be predicted well using NLR and ANN models. The compression strength tested using the BS standard was 71% higher than the compression strength of the same mix tested using the ASTM standard.
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