Determination of Bearing Capacity of Stone Column with Application of Neuro-fuzzy System

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Determination of Bearing Capacity of Stone Column with Application of Neuro-fuzzy System

Das, Manita / Dey, Ashim Kanti

Abstract The neuro-fuzzy controller applies the neural network learning techniques to tune the membership functions and keeps the semantics of the fuzzy logic controller intact. Hence benefits of both the neural network and fuzzy logic controller are taken into consideration. In this study, to predict the bearing capacity of a stone column, application of Adaptive Neuro-fuzzy Inference System (ANFIS) is presented. To train and test the data sets, 105 data pairs are collected from the previous technical literature. These data sets include the data of stone and sand columns. The spacing of the columns varies from 1.5 to 10 times the diameter. The undrained cohesion varies from 7 to 400 kPa. Both experimental and analytical data are included in the collection. To test the trained ANFIS models, data are collected from physical experiments on plate load test and numerical analysis with PLAXIS-2D. For the comparative study, ANFIS models combined with plate load test results and analytical results, three ANFIS models are developed. A comparative study on the accuracy of prediction by these three models is discussed.