Search Results (4)

Earthquakes and the secondary hazards they trigger, such as landslides, collapses, and debris flows, profoundly reshape the land surface and cause significant casualties, property damage and ecological disruption. This study collected 312 strong ground motion records from 19 seismic events in China, with magnitudes ranging from Ms5.2 to Ms8.0. Using the Newmark sliding block method and programming, permanent displacements for earthquake-induced landslides with varying yield accelerations were calculated. Two models (Model 1 and Model 2) for predicting permanent displacements of earthquake-induced landslides were developed through multiple regression analysis. Results show that the goodness of fit (R²) for the permanent displacement (logu) in Model 1 and Model 2 is 0.866 and 0.923, respectively. Model 2 incorporates higher-order terms of yield acceleration ratio (a_y/PGA), which effectively reduce nonlinearity in the residuals observed in Model 1 and enhance its accuracy. Finally, these models were compared with classical empirical models. Models 1 and 2, by calculating permanent displacement from ground motion data, provide critical insights into the mechanisms of earthquake-induced landslides, and play a key role in enhancing emergency response strategies for seismic geological hazards. Full article

Coseismic landslides have the potential to cause catastrophic disasters. Thus, it is of crucial importance to conduct a comprehensive regional assessment of susceptibility to coseismic landslides. This study rigorously interprets 13,759 coseismic landslides triggered by the 2022 Luding earthquake within the seismic zone. Employing the Newmark method, we systematically assess the susceptibility to coseismic landslides through the application of six distinct displacement regression models. The efficacy of these models is validated against the actual landslide inventory using the area under the receiver operating characteristic (ROC) curve. A hazard map of coseismic landslides is generated based on the displacement regression model with the highest degree of fit. The results show that Moxi Town, Detuo Town, the flanks of the Daduhe River, Wandonghe River, Hailuogou River, and Yanzigou River are high-susceptibility areas for coseismic landslides. This study explores factors influencing model fit, revealing that the inclusion of the epicentral distance and the distance to the seismogenic fault in displacement prediction enhances model performance. Nevertheless, in close proximity to fault zones, the distance to the seismogenic fault exerts a more significant influence on the spatial distribution density of coseismic landslides compared to the epicentral distance. Conversely, in regions situated further from fault zones, the epicentral distance has a greater impact on the spatial distribution density of coseismic landslides compared to the distance to the seismogenic fault. These findings contribute to a nuanced understanding of coseismic landslide susceptibility and offer valuable insights for future Newmark method-based coseismic landslide displacement calculations. Full article

Earthquake-triggered landslides have been widely recognized as a catastrophic hazard in mountainous regions. They may lead to direct consequences, such as property losses and casualties, as well as indirect consequences, such as disruption of the operation of lifeline infrastructures and delays in emergency response actions after earthquakes. Regional landslide hazard assessment is a useful tool to identify areas that are vulnerable to earthquake-induced slope instabilities and design prioritization schemes towards more detailed site-specific slope stability analyses. A widely used method to assess the seismic performance of slopes is by calculating the permanent downslope sliding displacement that is expected during ground shaking. Nathan M. Newmark was the first to propose a method to estimate the permanent displacement of a rigid body sliding on an inclined plane in 1965. The expected permanent displacement for a slope using the sliding block method is implemented by either selecting a suite of representative earthquake ground motions and computing the mean and standard deviation of the displacement or by using analytical equations that correlate the permanent displacement with ground motion intensity measures, the slope’s yield acceleration and seismological characteristics. Increased interest has been observed in the development of such empirical models using strong motion databases over the last decades. It has been almost a decade since the development of the latest empirical model for the prediction of permanent ground displacement for Greece. Since then, a significant amount of strong motion data have been collected. In the present study, several nonlinear regression-based empirical models are developed for the prediction of the permanent seismic displacements of slopes, including various ground motion intensity measures. Moreover, single-hidden layer Artificial Neural Network (ANN) models are developed to demonstrate their capability of simplifying the construction of empirical models. Finally, implementation of the produced modes based on Probabilistic Landslide Hazard Assessment is undertaken, and their effect on the resulting hazard curves is demonstrated and discussed. Full article

The mountainous region of Greater Sochi, including the Olympic ski-jump complex area, located in the northern Caucasus, is always subjected to landslides. The weathered mudstone of low strength and potential high-intensity earthquakes are considered as the crucial factors causing slope instability in the ski-jump complex area. This study aims to conduct a seismic slope instability map of the area. A slope map was derived from a digital elevation model (DEM) and calculated using ArcGIS. The numerical modelling of slope stability with various slope angles was conducted using Geostudio. The Spencer method was applied to calculate the slope safety factors (Fs). The pseudostatic analysis was used to compute Fs considering seismic effect. A good correlation between Fs and slope angle was found. Combining these data, sets slope instability maps were achieved. Newmark displacement maps were also drawn according to empirical regression equations. The result shows that the static safety factor map corresponds to the existing slope instability locations in a shallow landslide inventory map. The seismic safety factor maps and Newmark displacement maps may be applied to predict potential landslides of the study area in the case of earthquake occurrence. Full article