Speaker
Description
Using geotechnical engineering principles to assess slope stability typically uses a factor of safety to define the ratio of resisting to driving forces along a potential failure surface. Accurate estimation of the Factor of Safety is central to evaluating shallow landslide susceptibility, yet field-based measurements of soil strength are rarely available across steep, remote terrain. This challenge is particularly evident in New Zealand’s erodible hill-country. To address this limitation, this study develops a reproducible GIS- and literature-based workflow for computing factor of safety using environmental rasters and published geotechnical parameters, without requiring any in-situ testing.
To demonstrate the efficacy of the method, a case study area was selected in New Zealand where major storms triggered numerous shallow landslides in a plantation forestry area accurately mapped by forest owners.
The results demonstrate that a transparent, coding-based approach can generate physically interpretable FS estimates suitable for regional-scale screening in data-limited environments. The workflow establishes a foundation for future hybrid models in which FS-derived indicators can support, constrain, or enhance machine-learning landslide susceptibility frameworks.
| Keywords | Slope Safety; Landslides; Geotech; |
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