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Estimating the Geological Strength Index (GSI) in Regional Seismic-Landslide Zonation Using the Empirical Regression Model

Author(s): ORCID
ORCID

Medium: journal article
Language(s): English
Published in: Advances in Civil Engineering, , v. 2022
Page(s): 1-14
DOI: 10.1155/2022/4798523
Abstract:

The assessment of the strength parameters of geological formations in regional scale which encounters thousands of slopes is a complicated approach and time consuming and needs huge field work. This issue is an important research topic concerning the regional seismic-landslide susceptibility analysis or hazard zonation. An empirical regression model was presented to estimate the Geological Strength Index (GSI) with an implication on geological quadrangle of Gorgan region at Alborz mountains range (north of Iran). Two main sets of data were applied in this study: (1) geomorphological data including the slope height, aspect, and distance from faults and distance from thrusts and (2) the physical and mechanical properties of rocks including the unit weight, uniaxial compressive strength (σci), and the petrographic constant (mί) of intact rock. The first group was extracted from a 1 : 100,000 digital geologic map and 10 m digital elevation model (DEM) and the second group was obtained from the Hoek–Brown failure criterion recommended tables. Linear regression equations were generated applying data collected from 294 studied stations using SPSS software. The regression equation predicted GSI in terms of (1) the distance from faults, (2) the distance from thrusts, and (3) the uniaxial compressive strength (σci). The equation had an R2 value of 0.739 and thus fit well to the data. The new method in its present state was recommended for the estimation of the GSI values in regional scale conditions for the assessment of landslide susceptibility and hazard mapping or post events landslide occurrence prediction in the case of probable big earthquakes in Alborz area that is required for emergency responses. The results indicated that the estimation error was about ±30 while the average error was within +5 and −5 and average error percentage was about 3%.

Copyright: © 2022 M. E. Mirabedini et al. et al.
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10687209
  • Published on:
    13/08/2022
  • Last updated on:
    10/11/2022
 
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