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Deep Soil Mixing and Predictive Neural Network Models

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The work is basically a fusion of two existing research areas: Deep Soil Mixing (DSM) and Artificial Neural Networks (ANNs). The strength of the deep soil mixed walls and columns depends on a large number of factors which vary in a wide range and the dependence is complex. Soil type, grain size distribution, water content, plasticity index, liquidity index, clay content, organic matter content, binder type, water to binder ratio, mixing and curing conditions are few of the many variables that affect the strength development. The variability and uncertainty associated with these variables affect the magnitude of strength making the estimation of strength not a very straight forward task. To investigate this variability, data on a large number of these variables has been collated into a database from a large number of DSM projects worldwide. Uniformity and variability in the strength data have been analysed based on the data from these sources. The effect of parameters such as water content, liquidity index, cement content, total water to cement ratio and curing times on the UCS of the binder mixed soils has been studied by plotting these data together. The study has highlighted the importance of the variables such as liquidity index and total water to binder ratio which have not been extensively explored in previous DSM studies. Predictive Neural Network models which predict the strength gain of cement-stabilized clays as a function of clay water content, liquidity index, plasticity index, organic matter content, grain size distribution, cement content, total water to cement ratio and curing time have been developed. Results from the neural network models were found to emulate the known trends and reasonable estimates of strength as a function of the selected variables were obtained. The effectiveness of these data-driven non-linear predictive models is discussed.

This talk is part of the Engineering Department Geotechnical Research Seminars series.

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