Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic
Artificial neural networks and fuzzy logic approaches have recently been used to model some of the human activities in many areas of civil engineering applications. Especially from these systems in the model experimental studies, very good results have been obtained. In this research, the models for predicting compressive strength of mortars containing metakaolin at the age of 3, 7, 28, 60 and 90 days have been developed in artificial neural networks and fuzzy logic. For purpose of building these models, training and testing using the available experimental results for 179 specimens produced with 46 different mixture proportions were gathered from the technical literature. The data used in the multilayer feed-forward neural networks and Sugeno-type fuzzy inference models are arranged in a format of five input parameters that cover the age of specimen, metakaolin replacement ratio, waterbinder ratio, superplasticizer and bindersand ratio. According to these input parameters, in the multilayer feed-forward neural networks and Sugeno-type fuzzy inference models, the compressive strength of mortars containing metakaolin are predicted. The training and testing results in the multilayer feed-forward neural networks and Sugeno-type fuzzy inference models have shown that neural networks and fuzzy logic systems have strong potential for predicting compressive strength of mortars containing metakaolin.
compressive strength mortar metakaolin neural network fuzzy logic