TY - JOUR ID - 79229 TI - Performance Evaluation of RBF Networks with Various Variables to Forecast the Properties of SCCs JO - Civil Engineering Infrastructures Journal JA - CEIJ LA - en SN - 2322-2093 AU - Gholamzadeh Chitgar, Atefeh AU - Berenjian, Javad AD - Department of Civil Engineering, Tabari University of Babol, Babol, Iran AD - Faculty of Civil Engineering, Babol University of Technology, Babol – Iran Y1 - 2021 PY - 2021 VL - 54 IS - 1 SP - 59 EP - 73 KW - parameters KW - RBF Artificial Neural Networks KW - Self-Compacting Concrete KW - Test MSE DO - 10.22059/ceij.2020.288257.1611 N2 - In the present study, Radial Basis Function (RBF) neural networks are applied to forecast the compressive strength and elastic modulus of Self-Compacting Concrete (SCC). To construct the models, different experimental specimens of diverse kinds of SCC are gathered from the literature. The data used in the networks are classified into two different sets of input parameters. The results revealed that the proposed RBF models can accurately forecast the properties of SCCs with low test error. Furthermore, a comparison between models with two different sets of inputs proves that the selected parameters as input variables, straightly impress the precision of the networks, in the prediction of the intended outputs. UR - https://ceij.ut.ac.ir/article_79229.html L1 - https://ceij.ut.ac.ir/article_79229_33d0d38756b48e60aeaaa6af8573c827.pdf ER -