Reconstruction of landslide model from ERT, geotechnical, and field data, Nargeschal landslide, Iran

Rezaei, Sadegh; Shooshpasha, Issa; Rezaei, Hassan

doi:10.1007/s10064-018-1352-0

Cited by 37 publications

(17 citation statements)

References 62 publications

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“…In Moss et al [47] method, CSR is obtained using Eqn. (1). The rd in this method for d < 20 m (d = depth in meters) is defined as: where Rf is friction ratio in CPT (the ratio of sleeve to tip resistance, in percent).…”

Section: Moss Et Al Methodsmentioning

confidence: 99%

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Analysis of Soil Liquefaction Potential through Three Field Tests-Based Methods: A Case Study of Babol City

Hasanzadeh¹,

Rezaei²,

Shooshpasha³

et al. 2021

IJIE

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During earthquakes, ground failure is commonly caused by liquefaction. Thus, assessment of soil liquefaction potential in earthquake-prone regions is a crucial step towards reducing earthquake hazard. Since Babol city in Iran country is located in a high seismic area, estimation of soil liquefaction potential is of great importance in this city. For this purpose, in the present research, using field-based methods and geotechnical data (such as unit weight of soil, relative density, SPT number, shear wave velocity and cone tip resistance) of 60 available boreholes in Babol, three liquefaction maps were provided. Finally, one comprehensive liquefaction map was presented for soil of Babol city. The obtained results in this paper are well in line with the previous investigations. Based on the results, the factor of safety in 45% of the study area is less than one (liquefaction occurrence). In addition, the results indicate that since each field-based method requires particular data, applying various field tests is necessary for a more accurate liquefaction assessment.

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Section: Moss Et Al Methodsmentioning

confidence: 99%

“…Landslide and particularly liquefaction have been responsible for extensive damages to infrastructures and residential lands in recent earthquakes around the world [1]- [4]. Liquefaction phenomenon is associated with the development of large pore-water pressures in soils due to cyclic loading effects of earthquakes.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Soil Liquefaction Potential through Three Field Tests-Based Methods: A Case Study of Babol City

Hasanzadeh¹,

Rezaei²,

Shooshpasha³

et al. 2021

IJIE

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“…A landslide is defined as the movement of the slope covers, including soil, rock, and organic materials, under the influence of a gravitational force down the slope [1]. Among natural hazards (e.g., pollution, flooding, earthquakes, and landslides), landslides, the topic of this paper, rank seventh globally in terms of death and economic impact [2], including damage to roads rail lines, power lines, and touristic and historical [3][4][5]. Landslides can significantly affect the geomorphic evolution of the landscape that create some geological disasters throughout the world [6].…”

Section: Introductionmentioning

confidence: 99%

“…Debris and rock avalanches are relatively uncommon but are responsible for much loss of life. Landslides are particularly frequent in the Alborz and Zagros Mountains [4,9]. The risk posed by landslides in these areas is amplified by the inadequate scientific knowledge and resources aimed at dealing with the problem [5,10].…”

Section: Introductionmentioning

confidence: 99%

“…Considering the damage from the landslides particularly along the roads, monitoring the areas prone to landslide occurrence by preparing landslide susceptibility mapping is a mandatory for preventing and mitigating the most vulnerable areas [4,12,13]. According to the definition, landslide susceptibility refers to preventing and mitigating risk posed to the most vulnerable areas [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran

et al. 2020

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This paper aims to apply and compare the performance of the three machine learning algorithms–support vector machine (SVM), bayesian logistic regression (BLR), and alternating decision tree (ADTree)–to map landslide susceptibility along the mountainous road of the Salavat Abad saddle, Kurdistan province, Iran. We identified 66 shallow landslide locations, based on field surveys, by recording the locations of the landslides by a global position System (GPS), Google Earth imagery and black-and-white aerial photographs (scale 1: 20,000) and 19 landslide conditioning factors, then tested these factors using the information gain ratio (IGR) technique. We checked the validity of the models using statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC). We found that, although all three machine learning algorithms yielded excellent performance, the SVM algorithm (AUC = 0.984) slightly outperformed the BLR (AUC = 0.980), and ADTree (AUC = 0.977) algorithms. We observed that not only all three algorithms are useful and effective tools for identifying shallow landslide-prone areas but also the BLR algorithm can be used such as the SVM algorithm as a soft computing benchmark algorithm to check the performance of the models in future.

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Characterization of an active landslide structure with integrated electrical resistivity tomography and multi‐channel analysis of surface waves methods in Değirmendere district, Sakarya (Türkiye)

Karaaslan,

Silahtar,

Ramazanoglu

2023

Earth Surf Processes Landf

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Landslides following the development of slope instability can have serious consequences. Geophysical surveys have potential to aid in the understanding of landslides and their instability. This study described a landslide that occurred under particular geomorphological and meteorological conditions in Erenler (Sakarya, NW Türkiye) using electrical resistivity tomography (ERT) and multi‐channel analysis of surface waves (MASW) surveys and mechanical drillings. Data showed that the landslide is characterized by downslope movement of an altered clay unit over a claystone unit. The traditional landslide model used to interpret geoelectric data involves sliding of a relatively low‐resistivity material over higher resistivity bedrock. Here, the ERT survey showed a relatively high‐resistivity sliding material moving over relatively low‐resistivity bedrock units corresponding to the claystone unit. The relatively lower resistivity of the whole study area was related to the clay‐rich materials detected in the boreholes. This low‐resistivity unit was detected at high velocity in the MASW section, and it was understood that this layer was a compact structure acting like bedrock. The detected slip surfaces, landslide scars and slip surface toe indicate that the movement character of the landslide is retrogressive. The study results showed that the integrated and comparative use of different geophysical methods effectively reduces interpretation uncertainties and potential errors and leads to much better landslide characterization.

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Reconstruction of landslide model from ERT, geotechnical, and field data, Nargeschal landslide, Iran

Cited by 37 publications

References 62 publications

Analysis of Soil Liquefaction Potential through Three Field Tests-Based Methods: A Case Study of Babol City

Analysis of Soil Liquefaction Potential through Three Field Tests-Based Methods: A Case Study of Babol City

Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran

Characterization of an active landslide structure with integrated electrical resistivity tomography and multi‐channel analysis of surface waves methods in Değirmendere district, Sakarya (Türkiye)

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