Intergration of neural networks with NDE for concrete strength prediction
نویسندگان
چکیده
The application of neuro-fuzzy inference system to predict the compressive strengths of concrete is presented in this study. To investigate the influence of various parameters which affect the compressive strength, 2000 data samples were used for the analysis. Adaptive neuro-fuzzy inference system (ANFIS) was introduced for training and testing the data obtained from technical literatures. To reflect the effects of other uncertain parameters and in situ conditions, the results of non-destructive tests (NDT) such as ultrasonic pulse velocity (UPV) test and rebound hammer test were included as input parameters, in addition to mix proportion and curing histories. For the comparative study of the applicability of ANFIS models combined with NDT results, four ANFIS models were developed. These were classified depending on whether the input parameters of ANFIS models include NDT results or not. Among four models, ANFIS-UR model including both UPV test and rebound hammer test results shows best accuracy in the prediction of compressive strength.
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