Abstract
In this study, subtractive clustering algorithm (SCA) and fuzzy c-mean clustering (FCM) method were employed to construct an adaptive neuro-fuzzy inference system (ANFIS) model for the prediction of blast-induced ground vibration. To develop the ANFIS models, the charge weight per delay, distance, and scaled distance were taken into account as the input parameters, while peak particle velocity (PPV) was the output parameter. The performances of the both two ANFIS models and some conventional methods were compared in terms of three statistical indexes. The results shown that the FCM-ANFIS model can provide a precise evaluation of PPV if proper input data are provided.
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