PSI - Issue 62

Salvatore Misiano et al. / Procedia Structural Integrity 62 (2024) 576–584 Author name / Structural Integrity Procedia 00 (2019) 000 – 000

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Fig. 7. (a) 3D modeling of the preferential landslide path; (b) 2D view of the path and satellite image. In addition to the preferential path, it is assessed the evolution of the volume, according to section 2.3. Thus, the eroded depth of soil is evaluated in each step and the ∆ℎ is progressively summed to the initial height of the landslide body (initial depth of SLIP analysis). Hence, the initial estimated volume is around 2,300 cubic meters, and the final volume is approximately 19,100 cubic meters. The volume is assessed by multiplying the initial unstable area by the current height in each step. Unfortunately, there is no possibility of having a direct comparison with field data but, nevertheless, the final estimated volume appears as overestimated compared to common magnitudes, probably due to the excessive simplification of the flow dynamic of section 2.3. Furthermore, the estimated average speed of the debris flow is 13.7 m/s and the maximum peak is 21.45 m/s. These results, as before, cannot be compared with real data (not existing) but fulfill the common ranges for this type of events. 4. Conclusions The presented platform, and the applied modelling, showed a good overall predictive quality. SLIP model predicted concentrated areas of instability, within which is included the triggered area of the landslide that impacted the road SS117bis. The evolution model was extremely helpful in identifying the preferential path, which is in good agreement with the path that can be reconstructed from satellite imagery. Resulting volume quantities and speeds could be used in further analyses of impact with the infrastructures that intersect the possible landslide path. Regarding the latter properties, the obtained values must be interpreted as approximated, and this part of the problem will be the object of future studies, in order to refine them but maintaining the simplicity of the approach. As a matter of fact, it should be noted that the integrity and the simplification of the physical aspects allowed us to perform analyses for 11 different times (from 14:00 to 00:00 of the next day) in just 2.5 hours, paths included. References Borga, M., Dalla Fontana, G., Da Ros, D. et al., 1998: Shallow landslide hazard assessment using a physically based model and digital elevation data. In: Env. Geology 35, 81–88. Gariano, S.L., Guzzetti, F., 2016. Landslides in a changing climate. In: Earth - Sc. Rev., vol. 162. Gatto, M.P.A., Montrasio, L., 2023. X - SLIP: A SLIP - based multi - approach algorithm to predict the spatial - temporal triggering of rainfall - induced shallow landslides over large areas. In: Comput. Geotech. 154: 105175. Guthrie, R., Befus, A., 2021. DebrisFlow Predictor: an agent - based runout program for shallow landslides, Nat. Hazards Earth Syst. Sci., 21, 1029 1049. Iverson, R.M., Reid, M.E., 1992. Gravity - driven groundwater flow and slope failure potential: 1. Elastic Effective - Stress Model. Water Resour. Res., 28(3), 925–938. Jakob, M., Owen, T., 2021. Projected effects of climate change on shallow landslides, North Shore Mountains, Vancouver, Canada. In: Geomorphology 393, 107921. Komu, M.P., Nefeslioglu, H.A., Gokceoglu, C., 2023. A Review of the Prediction Methods for Landslide Runout. Proceedings, 87, 3.