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Effect of cement strength class on the prediction of compressive strength of cement mortar using GEP method
https://orcid.org/0000-0001-9156-6103
Graphical abstract Display Omitted
Highlights The influence of CSC on the compressive strength of cement mortar is evaluated. There is a good correlation between experimental and prediction results using GEP. Considering CSC as an additional input parameter lead to more accurate prediction.
Abstract Gene expression programming (GEP) has been widely used to predict the properties of cementation materials. In this study, 54 mix designs including six water/cement (W/C) ratios of 0.25, 0.30, 0.35, 0.40, 0.45 and 0.50, three sand/cement (S/C) ratios of 2.50, 2.75, and 3.00 as well as three cement strength classes (CSC) of 32.5, 42.5 and 52.5 MPa were first constructed and then the compressive strength of 270 constructed samples with five different ages of 3, 7, 14, 21 and 28 days was measured. The compressive strength of cement mortar was then predicted using GEP and the results were utilized to investigate the roles of linking function and CSC on the performance of GEP models. The effect of CSC on the prediction of compressive strength of cement mortar was also evaluated by comparing the prediction results obtained from the proposed GEP in the current study considering CSC as an extra input parameter with the prediction results taken from existing GEP model from the literature without considering the CSC, where the input parameters were collected from three data sets of previous studies from the literature. The results showed that the GEP model with linking function of addition has a better performance than that with linking function of multiplication. The results also showed the strong potential of proposed GEP in predicting the compressive strength of cement mortar. Furthermore the results showed that considering the CSC as an additional input data increases the prediction accuracy of compressive strength.
Effect of cement strength class on the prediction of compressive strength of cement mortar using GEP method
https://orcid.org/0000-0001-9156-6103
Graphical abstract Display Omitted
Highlights The influence of CSC on the compressive strength of cement mortar is evaluated. There is a good correlation between experimental and prediction results using GEP. Considering CSC as an additional input parameter lead to more accurate prediction.
Abstract Gene expression programming (GEP) has been widely used to predict the properties of cementation materials. In this study, 54 mix designs including six water/cement (W/C) ratios of 0.25, 0.30, 0.35, 0.40, 0.45 and 0.50, three sand/cement (S/C) ratios of 2.50, 2.75, and 3.00 as well as three cement strength classes (CSC) of 32.5, 42.5 and 52.5 MPa were first constructed and then the compressive strength of 270 constructed samples with five different ages of 3, 7, 14, 21 and 28 days was measured. The compressive strength of cement mortar was then predicted using GEP and the results were utilized to investigate the roles of linking function and CSC on the performance of GEP models. The effect of CSC on the prediction of compressive strength of cement mortar was also evaluated by comparing the prediction results obtained from the proposed GEP in the current study considering CSC as an extra input parameter with the prediction results taken from existing GEP model from the literature without considering the CSC, where the input parameters were collected from three data sets of previous studies from the literature. The results showed that the GEP model with linking function of addition has a better performance than that with linking function of multiplication. The results also showed the strong potential of proposed GEP in predicting the compressive strength of cement mortar. Furthermore the results showed that considering the CSC as an additional input data increases the prediction accuracy of compressive strength.
Effect of cement strength class on the prediction of compressive strength of cement mortar using GEP method
https://orcid.org/0000-0001-9156-6103
Graphical abstract Display Omitted
Highlights The influence of CSC on the compressive strength of cement mortar is evaluated. There is a good correlation between experimental and prediction results using GEP. Considering CSC as an additional input parameter lead to more accurate prediction.
Abstract Gene expression programming (GEP) has been widely used to predict the properties of cementation materials. In this study, 54 mix designs including six water/cement (W/C) ratios of 0.25, 0.30, 0.35, 0.40, 0.45 and 0.50, three sand/cement (S/C) ratios of 2.50, 2.75, and 3.00 as well as three cement strength classes (CSC) of 32.5, 42.5 and 52.5 MPa were first constructed and then the compressive strength of 270 constructed samples with five different ages of 3, 7, 14, 21 and 28 days was measured. The compressive strength of cement mortar was then predicted using GEP and the results were utilized to investigate the roles of linking function and CSC on the performance of GEP models. The effect of CSC on the prediction of compressive strength of cement mortar was also evaluated by comparing the prediction results obtained from the proposed GEP in the current study considering CSC as an extra input parameter with the prediction results taken from existing GEP model from the literature without considering the CSC, where the input parameters were collected from three data sets of previous studies from the literature. The results showed that the GEP model with linking function of addition has a better performance than that with linking function of multiplication. The results also showed the strong potential of proposed GEP in predicting the compressive strength of cement mortar. Furthermore the results showed that considering the CSC as an additional input data increases the prediction accuracy of compressive strength.
Effect of cement strength class on the prediction of compressive strength of cement mortar using GEP method
Mahdinia, Sahar (author) / Eskandari-Naddaf, Hamid (author) / Shadnia, Rasoul (author)
Construction and Building Materials ; 198 ; 27-41
2018-11-27
15 pages
Article (Journal)
Electronic Resource
English
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