Spatial Interpolation of consolidation properties of Holocene clays at Kobe Airport using an artificial neural network
Keywords:
Neural Network, Geoinformatics, Spatial Interpolation, Holocene Clay, Soil PropertiesAbstract
The spatial distribution of the consolidation properties for a seabed must be appropriately estimated to accurately predict the consolidation settlement due to large-scale reclamation. The soil properties must be estimated at arbitrary positions in the ground from data collected during soil investigation. In this study, an artificial neural network was applied to spatially interpolate consolidation properties such as the natural water content, void ratio, plastic index, compression index, and pre-consolidation pressure. The estimation accuracy of consolidation properties was judged based on four indexes: R2, G, MARE, and SR. The artificial neural network estimated the appropriate consolidation properties with high accuracy; this confirmed the availability of spatial interpolation of consolidation properties by using an artificial neural network