Mechanism of large-scale landslides in residential valley fills induced by earthquake

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Investigation of past artificial landform changes and multi-variate analysis of case studies of past earthquake disasters show that differences in the shape of fills, such as depth, width, inclination angle of the base, and cross-sectional form, may be the key discriminating factors of slope instability. Triggering mechanisms (e.g. earthquakes) need to be considered in the analysis for accurate estimation, however, it is difficult to include earthquake parameters in such linear multi-variate analysis (quantification theory II). Neural network analysis is applied to assess large fill slope instability in urban residential areas. The developed neural network model including both causative factors (shape of fills, groundwater condition, age of construction) and the triggering factors (distance from the fault, moment magnitude, direction to fault) was independently checked against another data set and sensitivity analysis was conducted. The high hitting ratio over 90 % in the application to the earthquakes in 2003 revealed that the proposed model should be capable of wide application to conduct landslide hazard mapping in urban residential areas.Observation of strong earthquake motion and pore water pressure in valley fill shows that the response on the fill should be smaller than the acceleration on the original ground. This attenuation effect of the soft filling material was also verified by FEM analysis. Based on the inventories and observation results, the roller-slider model was proposed as the mechanism of the valley fill type landslide. The quasi-3D stability analysis based on the roller-slider model should be capable to explain the inventories of the past earthquake disaster compared with the poor result of the conventional 2D analysis. It means that both the failure at the base of the valley and constraint by the side walls should be significant mechanism of the artificial valley fill type landslide.