Cihan, Mehmet TimurAral, İbrahim Feda2022-05-112022-05-1120221598-81981598-818Xhttps://doi.org/10.12989/cac.2022.29.3.187https://hdl.handle.net/20.500.11776/4613The usability of waste materials as raw materials is necessary for sustainable production. This study investigates the effects of different powder materials used to replace cement (0%, 5% and 10%) and standard sand (0%, 20% and 30%) (basalt, limestone, and dolomite) on the compressive strength (f(c)), flexural strength (f(r)), and ultrasonic pulse velocity (UPV) of mortars exposed to freeze-thaw cycles (56, 86, 126, 186 and 226 cycles). Furthermore, the usability of artificial intelligence models is compared, and the prediction accuracy of the outputs is examined according to the inputs (powder type, replacement ratio, and the number of cycles). The results show that the variability of the outputs was significantly high under the freeze-thaw effect in mortars produced with waste powder instead of those produced with cement and with standard sand. The highest prediction accuracy for all outputs was obtained using the adaptive-network-based fuzzy inference system model. The significantly high prediction accuracy was obtained for the UPV, f(c), and f(r) of mortars produced using waste powders instead of standard sand (R-2 of UPV, f(c )and f(r) is 0.931, 0.759 and 0.825 respectively), when under the freeze-thaw effect. However, for the mortars produced using waste powders instead of cement, the prediction accuracy of UPV was significantly high (R-2=0.889) but the prediction accuracy of f(c) and f(r) was low (R(2)f(c)=0.612 and R(2)f(r)=0.334).en10.12989/cac.2022.29.3.187info:eu-repo/semantics/closedAccessartificial intelligencefreeze-thaw effectmortarwaste powderSelf-Compacting ConcreteMultiple Linear-RegressionArtificial Neural-NetworkCompressive StrengthFly-AshCement MortarsPumice PowderStone DustGraniteMarbleArticle293187199Q1WOS:000773462100005