Preprint Article Version 1 This version is not peer-reviewed

Development of Statistics-Based Estimation Model (SEM) for Landslide-Triggering Factors Using the Soil Physical Properties of the Landslide in Gneiss and Granite Areas of South Korea

Version 1 : Received: 9 October 2019 / Approved: 10 October 2019 / Online: 10 October 2019 (14:53:30 CEST)

How to cite: Kim, K.; Kim, M.; Lee, M.; Hwang, E. Development of Statistics-Based Estimation Model (SEM) for Landslide-Triggering Factors Using the Soil Physical Properties of the Landslide in Gneiss and Granite Areas of South Korea. Preprints 2019, 2019100118 (doi: 10.20944/preprints201910.0118.v1). Kim, K.; Kim, M.; Lee, M.; Hwang, E. Development of Statistics-Based Estimation Model (SEM) for Landslide-Triggering Factors Using the Soil Physical Properties of the Landslide in Gneiss and Granite Areas of South Korea. Preprints 2019, 2019100118 (doi: 10.20944/preprints201910.0118.v1).

Abstract

In South Korea, landslides are caused by localized heavy rainfall and typhoons, which often occur in the summer season at natural slopes in mountainous areas and artificial slopes in urban surroundings. Flow-type landslides frequently occur in mountainous areas. To evaluate flow-type landslides, it is essential to identify the physical characteristics of soil, giving focus to the soil on the top layers of various types of slope. This study conducts a survey and an analysis of the characteristics of landslides that occurred in the study area with different geological conditions of granite and gneiss. The characteristics of soil in the area and its surroundings that have or have not undergone landslides for every geological condition is also evaluated. Based on these characteristics and a statistics method, it extracts the triggering factors, permeability coefficients (k), and shearing strength with cohesion (c) and internal friction angel (φ) of soils that are highly related to landslides around weathered soil layers. As a result, the permeability coefficients show significant relevance with void ratio (e), the effective size of grains (D10), and uniformity coefficient (cu), while the shearing strength with the proportion of fine-grained soil (Fines), uniformity coefficient (cu), degree of saturation (S), dry weight density (rd), and void ratio (e). By obtaining this result, the study uses the regression analysis to suggest models to estimate the permeability coefficients and shearing strength. For the gneiss area, the statistics-based estimation model (SEM) is proposed as kgn = (1.488 × 10-02 × e) + (1.076 × D10) + (-1.629 × 10-04 × cu) - (1.893 × 10-02) for permeability coefficients; cgn = (-0.712 × Fines) + (-0.131 × cu) + 15.335 for cohesion; and φgn = (27.01 × rd) + (-12.594 × e) + 6.018 for internal frictional angle of soils. For the granite area, the statistics-based estimation model (SEM) is proposed as kgr = (8.281 × 10-03 × e) + (0.639 × D10) + (-2.766 × 10-05 × cu) - (9.907 × 10-03) for permeability coefficients; cgr = (-0.689 × Fines) + (-0.0744 × S) + 18.59 for cohesion; and φgr = (33.640 × rd) + (-0.875 × e) - 9.685 for internal frictional angle of soils. The use of statistics-based estimation models (SEMs) for landslide-triggering factors that trigger landslides will support the simple calculation of permeability coefficient and shearing strength (cohesion and internal frictional angle), only requiring information about the physical properties of soil at the natural slopes that have different geological features such as gneiss and granite areas.

Subject Areas

statistics-based estimation model (sem); different geological condition; permeability coefficient; shearing strength; landslide-triggering factor

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