Evaluation of road network slopes susceptibility to seismically-induced landslides in the Granada Basin (S Spain)

Abstract

In this work, a method for the fast evaluation of seismically-induced landslides is applied at regional scale in the Granada Basin (southern Spain). The methodology is based on the concept of rigid block but takes into account the variability in input data (geotechnical properties, failure depth and saturation state of the materials) through the use of a logical tree scheme. Input data were combined and evaluated using five different Newmark displacement empirical relations, which are automatically weighted to obtain a final co-seismic landslide susceptibility map. A Geographic Information System system was used to obtain the resulting maps considering the different branches and weights of the logic tree through an specifically developed automatic code written in Phyton. The landslides induced by the 2021 Mw 4.4 events of the Santa Fe seismic series have been used to validate the methodology. This is later used as input along with the road network to analyze the distribution of areas of different susceptibility level. The results obtained show that 1.73% of the total area is characterized by moderate to high susceptibility, being the southern and eastern sections of the Granada basin were more vulnerable slopes are found. When considering only zones around the road network (50 m buffer), it is found that 2.11% of slopes around highways and 1.33% of road slopes have moderate to high susceptibility, especially the A-92 highway and the A-338 road. These roads traverse areas with steep slopes and a combination of hard and soft materials, which increases the susceptibility of disrupted landslides. On the other hand, in more densely populated areas such as the metropolitan area of Granada, the susceptibility is lower due to their location on terrain with low to negligible slopes. The results are of interest for authorities managing slope stabilities because they allow establishing effective and locally-based seismic forecast works in order to minimize the damage of future events.