Estimating the Geological Strength Index (GSI) in Regional Seismic-Landslide Zonation Using the Empirical Regression Model
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 analysi...
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2022-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/4798523 |
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| author | M. E. Mirabedini E. Haghshenas N. Ganjian |
| author_facet | M. E. Mirabedini E. Haghshenas N. Ganjian |
| author_sort | M. E. Mirabedini |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-0acef4032fe84372a5bc38adee293289 |
| institution | Kabale University |
| issn | 1687-8094 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-0acef4032fe84372a5bc38adee2932892025-02-03T01:32:29ZengWileyAdvances in Civil Engineering1687-80942022-01-01202210.1155/2022/4798523Estimating the Geological Strength Index (GSI) in Regional Seismic-Landslide Zonation Using the Empirical Regression ModelM. E. Mirabedini0E. Haghshenas1N. Ganjian2Department of Civil EngineeringInternational Institute of Earthquake Engineering and Seismology (IIEES)Department of Civil EngineeringThe 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%.http://dx.doi.org/10.1155/2022/4798523 |
| spellingShingle | M. E. Mirabedini E. Haghshenas N. Ganjian Estimating the Geological Strength Index (GSI) in Regional Seismic-Landslide Zonation Using the Empirical Regression Model Advances in Civil Engineering |
| title | Estimating the Geological Strength Index (GSI) in Regional Seismic-Landslide Zonation Using the Empirical Regression Model |
| title_full | Estimating the Geological Strength Index (GSI) in Regional Seismic-Landslide Zonation Using the Empirical Regression Model |
| title_fullStr | Estimating the Geological Strength Index (GSI) in Regional Seismic-Landslide Zonation Using the Empirical Regression Model |
| title_full_unstemmed | Estimating the Geological Strength Index (GSI) in Regional Seismic-Landslide Zonation Using the Empirical Regression Model |
| title_short | Estimating the Geological Strength Index (GSI) in Regional Seismic-Landslide Zonation Using the Empirical Regression Model |
| title_sort | estimating the geological strength index gsi in regional seismic landslide zonation using the empirical regression model |
| url | http://dx.doi.org/10.1155/2022/4798523 |
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