A comparative modeling study to estimate wear of concrete
This paper investigates the impacts of fuzzy genetic (FG), a new fuzzy logic model with genetic algorithm, artificial neural networks (ANN) and general linear model (GLM) approaches on abrasive wear of concrete. For this purpose, experimental studies were made to investigate the influence on wear of the following input parameters: hematite, cement, compressive strength and different loads on the experiments. In these models, 60 data sets were used. For training set, 48 data (80%) were randomly selected and the residual data (12 data, 20%) were test set. Model results were compared with experimental results. In this paper, main model performance criterion was root mean square errors. Also, sum of squared error and determination coefficient statistics were used as comparing criteria for the evaluation of models performances. Comparison results indicate that FG models are superior to ANN and GLM models in modeling of influence hematite, cement, compressive strength and loads on wear of concrete.
wear concrete fuzzy genetic artificial neural networks general linear model