Recent computational models create a large amount of data, which can be hard to analyze. In this paper, we demonstrate the power of employing data analytics techniques to characterize soil behavior during liquefaction. We used simple simulation output aggregated over several locations along the depth of the deposit. Available were five simulated quantities (shear strain, acceleration, coordination number, pore pressure, change in volume) and we added four change features (direction and magnitude of change of the quantity). We then performed data mining using conventional analytics methods (clustering using k-means with k = 5 and principal components analysis). The clustering visualization showed that a visible break started to propagate downward on the onset of liquefaction through all depth locations. Using this scheme, we were able to build cluster profiles that generate new insights by visualizing details of the transformation process of the soil from a solid state to a liquefied state.
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