Predictive model of Arias intensity and Newmark displacement for regional scale evaluation of earthquake-induced landslide hazard in Greece

Chousianitis, Konstantinos; Gaudio, Vincenzo Del; Kalogeras, I.; Ganas, Athanassios

doi:10.1016/j.soildyn.2014.05.009

Cited by 96 publications

(55 citation statements)

References 63 publications

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“…The intense ground motion could cause a short-lived distribution in the balance of forces within natural slopes, which is the main reason for landslides. The Arias intensity is the most suitable for characterizing earthquake impacts (Hsieh et al 2011;Chousianitis et al 2014), and it is estimated by the integration of the squared acceleration overtime. However, owing to the fact that the Arias intensity incorporates more information content, a single parameter, the most commonly used parameter to describe earthquake ground motion is PGA Li et al 2013a;Hsieh et al 2011;Chousianitis et al 2014;Li et al 2013b;Tian et al 2017).…”

Section: Identification Of Influencing Factorsmentioning

confidence: 99%

Application and comparison of logistic regression model and neural network model in earthquake-induced landslides susceptibility mapping at mountainous region, China

Xie

Wen

et al. 2018

Geomatics, Natural Hazards and Risk

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The main objective of this study is to evaluate the performances of different earthquake-induced landslides susceptibility mapping models at mountainous regions in China. At first, 160 earthquake-induced landslide points were identified from field investigations. Concurrently, based on the results of a literature review and the field investigation, 12 influencing factors were considered, and the corresponding thematic layers were generated using geographic information system (GIS) technology. Subsequently, 20 groups with a fixed number of cells were collected as a common training dataset for the two different models, based on a random selection from the entire database (including landslide cells and no-landslide cells). The neural network (NN) model and logistic regression (LR) model were developed with R software. Finally, earthquake-induced landslides susceptibility maps of Wenchuan county were produced, very low, low, medium, high and very high susceptibility zones cover. The validation results indicate that the landslide data from field investigations are in good agreement with the evaluation results, and the LR model has a slightly better prediction than the NN model in this case. In general, the NN model and LR models are satisfactory for susceptibility mapping of earthquake-induced landslides at mountainous regions.

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Section: Identification Of Influencing Factorsmentioning

confidence: 99%

Application and comparison of logistic regression model and neural network model in earthquake-induced landslides susceptibility mapping at mountainous region, China

Xie

Wen

et al. 2018

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

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“…Meanwhile, in the 3D domain with two horizontal components the analyses usually apply either the sum of both components or the largest value out of them (see, e.g. Del Gaudio and Wasowski 2011;Chousianitis et al 2014) In Eq. (5) the surface acceleration-time histories are analysed in their original way, while reference histories are modified prior to integration.…”

Section: General Patterns Of the Arias Intensity Amplificationmentioning

confidence: 99%

“…Travasarou et al 2003) or through the site variables reflecting the soil/rock site composition (see, e.g. Sabetta and Pugliese 1996;Rathje et al 2004;Romeo 2000;Stafford et al 2009;Chousianitis et al 2014).…”

Section: Introductionmentioning

confidence: 99%

2D dynamic studies combined with the surface curvature analysis to predict Arias Intensity amplification

Torgoev

Havenith

2016

J Seismol

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A 2D elasto-dynamic modelling of the pure topographic seismic response is performed for six models with a total length of around 23.0 km. These models are reconstructed from the real topographic settings of the landslide-prone slopes situated in the Mailuu-Suu River Valley, Southern Kyrgyzstan. The main studied parameter is the Arias Intensity (Ia, m/sec), which is applied in the GIS-based Newmark method to regionally map the seismically-induced landslide susceptibility. This method maps the Ia values via empirical attenuation laws and our studies investigate a potential to include topographic input into them. Numerical studies analyse several signals with varying shape and changing central frequency values. All tests demonstrate that the spectral amplification patterns directly affect the amplification of the Ia values. These results let to link the 2D distribution of the topographically amplified Ia values with the parameter called as smoothed curvature. The amplification values for the low-frequency signals are better correlated with the curvature smoothed over larger spatial extent, while those values for the high-frequency signals are more linked to the curvature with smaller smoothing extent. The best predictions are provided by the curvature smoothed over the extent calculated according to Geli's law. The sample equations predicting the Ia amplification based on the smoothed curvature are presented for the sinusoid-shape input signals. These laws cannot be directly implemented in the regional Newmark method, as 3D amplification of the Ia values addresses more problem complexities which are not studied here. Nevertheless, our 2D results prepare the theoretical framework which can potentially be applied to the 3D domain and, therefore, represent a robust basis for these future research targets.

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“…Geologic, seismic and site-specific factors determine the damage caused by earthquakes including earthquake magnitude, the depth of the earthquake, seismic wave attenuation, geological structure of the affected area, type and quality of construction, the location of the epicentre of the earthquake and population density. Among these geologic and seismic parameters that characterize ground shaking associated with earthquakes are peak ground acceleration and Arias intensity, which have been analyzed in recent major earthquakes in Italy, Greece and Japan (Del Gaudio & Wasowski 2011;Goda et al 2013;Chousianitis et al 2014). Some researchers have also estimated fatality as a function of the time of day the earthquake occurred (Lomnitz 1970), number of completely destroyed houses (Ohta et al 1983), earthquake magnitude (Oike 1991) and population density (Samardjieva & Badal 2002).…”

Section: Introductionmentioning

confidence: 99%

Geographic analysis of earthquake damage in Turkey between 1900 and 2012

Gökkaya

2016

Geomatics, Natural Hazards and Risk

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This study investigates the relationship between fatality and magnitude, energy released, time of earthquake occurrence and building damage, and between building damage and magnitude and energy of earthquakes that occurred in Turkey between 1900 and 2012. Patterns of earthquake occurrence and damage are examined across space and time. Ninety-one per cent and eighty-three per cent of the variation in fatality and building damage are accounted for by the energy released from an earthquake, respectively. Building damage explains 85% of the variation in fatality. Seventy-six per cent of the total death toll is a result of the earthquakes that happened between midnight and 5:22 am. There were two significant clusters of earthquakes in Western Turkey between 1955 and 1965 and between 1969 and 1971. Provinces of Erzincan and Kocaeli emerge as hot spots of fatality and building damage. The location of major earthquakes over 112 years tends to parallel the tectonically active areas of Turkey such as the North and East Anatolian Faults and Western Turkey towards the Aegean Sea. Central, central south, extreme north-western, north-eastern and south-eastern Turkey appears to be void of major earthquakes.

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Predictive model of Arias intensity and Newmark displacement for regional scale evaluation of earthquake-induced landslide hazard in Greece

Cited by 96 publications

References 63 publications

Application and comparison of logistic regression model and neural network model in earthquake-induced landslides susceptibility mapping at mountainous region, China

Application and comparison of logistic regression model and neural network model in earthquake-induced landslides susceptibility mapping at mountainous region, China

2D dynamic studies combined with the surface curvature analysis to predict Arias Intensity amplification

Geographic analysis of earthquake damage in Turkey between 1900 and 2012

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