Earthquake risk assessments of large residential fill-slope in urban areas

Abstract

Recent destructive earthquakes in urban regions, such as the 1978 Miyagiken-oki earthquake, the 1993 Kushiro-oki earthquake and the 1995 Hyougoken-nanbu earthquake, have triggered landslides in many gentle slopes of residential areas around Sendai, Kushiro, Ni shinomiya and Kobe. The earthquake-induced slope instability that has occurred is closely related to these artificial landforms, especially valley fills (embankments). More than 60% of the unstable slopes in the Kobe-Nishinomiya urban region are in artificial valley fills. This instability was caused by strong ground movements during the 1995 Hyougoken -nanbu earthquake.
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 the -ory 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 neural network model appears to have advantages over the multi -variate analysis . It should be possible to conduct landslide hazard mapping in urban residential areas by using the newly proposed neural network model.