Earthquake-induced Landslide Hazard Research Articles

Examining the contribution of lithology and precipitation to the performance of earthquake-induced landslide hazard prediction

Earthquake-induced landslides (EQIL) are one of the most catastrophic geological hazards. Immediate and swift evaluation of EQIL hazard in the aftermath of an earthquake is critically important and of substantial practical value for disaster reduction. The selection of influencing factor layers is crucial when using machine learning methods to predict EQIL hazard. As important input factors for EQIL hazard models, lithology and precipitation are extensively employed in forecasting EQIL hazard. However, few work explored whether these layers can improve the accuracy of EQIL hazard predictions. With Random Forest (RF) models, we employed a traditional and a state-of-the-art sampling strategy to assess EQIL modelling with and without lithology and precipitation data for the 2022 Luding earthquake in China. First, by excluding both factors, we used eight other influencing factors (land use, slope aspect, slope, elevation, distance to faults, distance to rivers, NDVI, and peak ground acceleration) to generate a landslide hazard map. Second, lithology and precipitation were separately added to the original EQIL hazard models. The results indicate that neither lithology nor precipitation have positive effects on the prediction of EQIL for both sampling strategies. The high-risk areas (or low-risk areas) tend to cluster within certain lithology types or precipitation ranges, which significantly affects the accuracy of the hazard map. Additionally, the model with the state-of-the-art sampling strategy deteriorates more than the model with the traditional sampling strategy. We believe this is very likely due to the strong spatial clustering of negative sample points caused by the latest sampling strategy. Our findings will contribute to the assessment of post-earthquake landslide hazards and the advancement of emergency disaster mitigation efforts.

Incorporating Effects of Slope Units and Sliding Areas into Seismically Induced Landslide Risk Modeling in Tectonically Active Mountainous Areas

Traditional Newmark models estimate earthquake-induced landslide hazards by calculating permanent displacements exceeding the critical acceleration, which is determined from static factors of safety and hillslope geometries. However, these studies typically predict the potential landslide mass only for the source area, rather than the entire landslide zone, which includes both the source and sliding/depositional areas. In this study, we present a modified Newmark Runout model that incorporates sliding and depositional areas to improve the estimation of landslide chain risks. This model defines the landslide runout as the direction from the source area to the nearest river channel within the same slope unit, simulating natural landslide behavior under gravitational effects, which enables the prediction of the entire landslide zone. We applied the model to a subset of the Minjiang Catchment affected by the 1933 MW 7.3 Diexi Earthquake in China to assess long-term landslide chain risks. The results indicate that the predicted total landslide zone closely matches that of the Xinmo Landslide that occurred on 24 June 2017, despite some uncertainties in the sliding direction caused by the old landslide along the sliding path. Distance-weighted kernel density analysis was used to reduce the prediction uncertainties. The hazard levels of the buildings and roads were determined by the distance to the nearest entire landslide zone, thereby assessing the landslide risk. The landslide dam risks were estimated using the kernel density module for channels blocked by the predicted landslides, modeling intersections of the total landslide zone and the channels. High-risk landslide dam zones spatially correspond to the locations of the knickpoints primarily induced by landslide dams, validating the model’s accuracy. These analyses demonstrate the effectiveness of the presented model for Newmark-based landslide risk estimations, with implications for geohazard chain risk assessments, risk mitigation, and land use planning and management.

Application of different earthquake-induced landslide hazard assessment models on the 2022 Ms 6.8 luding earthquake

Following the earthquake, prompt evaluation of the distribution of coseismic landslides and estimation of potential disaster losses are crucial for emergency response and resettlement planning. The Luding earthquake of 2022 offers a valuable opportunity to conduct a rapid assessment of coseismic landslides using various models. In this study, we utilize the Logistic Regression (LR)-based Xu2019 model, a new-generation model developed in China, alongside the Newmark model to perform the rapid hazard assessment of coseismic landslides. Assessing the accuracy and applicability of these two models based on the coseismic landslides from the Luding earthquake, we find that within intensity area of IX, the high probability area identified by the Newmark model aligns closely with the actual distribution of landslides. However, the Newmark model’s prediction is overestimated in the intensity area of VIII. For the Xu2019 model, the prediction results are in good agreement with the distribution of actual landslides. Most landslides are located in high probability areas, such as Detuo town, Wandong, and Xingfu villages, indicating that the model has a higher prediction accuracy. Overall, two models have good practical utility in emergency hazard assessment of coseismic landslides. However, the Newmark model requires multi-input parameters and the assignment of these parameters will increase the uncertainty and subjectivity in the practical application of the modeling assessment.

Predictive Displacement Models Considering the Probability of Pulse-Like Ground Motions for Earthquake-Induced Landslides Hazard Assessment

ABSTRACT Predictive displacement models (PDMs) based on the Newmark sliding block method have been widely applied in earthquake-induced landslide hazard assessment (ELHA). Pulse-like ground motions (PLGMs) can cause significant structural damage. In this study, four PDMs that consider the probability of occurrence of PLGMs (PP) were created for strike-slip and non-strike-slip events. The results demonstrate that the model with PP has greater accuracy in non-strike-slip events than the model without PP, however, the effect of PP may be disregarded in strike-slip events. This result is applicable to the ELHA and can provide a basis for post-earthquake emergency and geological disaster mitigation.

Assessment of earthquake-induced landslide hazard zoning using the physics-environmental coupled Model

Assessment of earthquake-induced landslide hazard zoning using the physics-environmental coupled Model

Comparison between Machine Learning and Physical Models Applied to the Evaluation of Co-Seismic Landslide Hazard

A comparative methodology between advanced statistical tools and physical-based methods is carried out to ensure their reliability and objectivity for the evaluation of co-seismic landslide hazard maps. To do this, an inventory of landslides induced by the 2011 Lorca earthquake is used to highlight the usefulness of these methods to improve earthquake-induced landslide hazard analyses. Various statistical models, such as logistic regression, random forest, artificial neural network, and support vector machine, have been employed for co-seismic landslide susceptibility mapping. The results demonstrate that machine learning techniques using principal components (especially, artificial neural network and support vector machine) yield better results compared to other models. In particular, random forest shows poor results. Artificial neural network and support vector machine approaches are compared to the results of physical-based methods in the same area, suggesting that machine learning methods can provide better results for developing co-seismic landslide susceptibility maps. The application of different advanced statistical models shows the need for validation with an actual inventory of co-seismic landslides to ensure reliability and objectivity. In addition, statistical methods require a great amount of data. The results establish effective land planning and hazard management strategies in seismic areas to minimize the damage of future co-seismic landslides.

Evaluating earthquake-induced widespread slope failure hazards using an AHP-GIS combination

Landslides and slope failures are often caused by earthquakes. This study proposes a method to map earthquake-induced slope failure hazards that uses the analytic hierarchy process (AHP) and a geographic information system (GIS) for four districts where many slope failures were induced by earthquakes (the 2018 Hokkaido Eastern Iburi, 2016 Kumamoto, 2008 Iwate-Miyagi Nairiku, and 2004 Mid Niigata Prefecture earthquakes). The assessment system, which was based on the National Research Institute for Earth Science and Disaster Resilience landslide distribution maps, was analyzed using the methods of previously published. We considered the relationships between the earthquake-induced slope failure distributions and landslide hazard factors (elevation, slope angle, slope type, catchment degree, geology, and vegetation). These relationships were utilized for pairwise comparisons of the factors in the AHP analysis. The slope angle, slope type, and catchment degree exerted the highest effects on the slope failure distribution in the four districts. The four earthquake-induced slope failure distributions were highly consistent with the slope failure hazard rank. These results provide a practical method for evaluating earthquake-induced slope-failure hazards.

Application of novel deep boosting framework-based earthquake induced landslide hazards prediction approach in Sikkim Himalaya

A major earthquake (6.9 Moment magnitude) occurred in the Sikkim and Darjeeling areas of the Indian Himalaya as well as in the adjacent Nepal on 18th September 2011, triggering a large number of landslides. A total of 188 landslide locations were extracted in order to create the landslide inventory map (LIM). The earthquake-induced landslide susceptibility maps (LSMs) were created using an Artificial Neural Network (ANN) model and three novel deep learning approaches (DLAs), namely Deep Boosting (DB), Deep Learning Neural Network (DLNN), and Deep Learning Tree (DLT), as well as training points and 22 conditioning factors. The earthquake-induced LSMs validated using several statistical indices and the results showed optimal accuracy for all models, where DB yielding the highest prediction rate curve (PRC) of 98.5%. This is followed by DLT (97%), DLNN (96%), and ANN (91%). The results demonstrate maximum efficacy of the proposed earthquake-induced LSM.

Influence of critical acceleration model on assessments of potential earthquake–induced landslide hazards in Shimian County, Sichuan Province, China

Critical acceleration is an inherent property of a slope and determines the slope stability under seismic action. The critical acceleration model is a core element of regional seismic landslide hazard assessment. Therefore, the purpose of this paper is to reveal the influence of different critical acceleration models on assessments of potential earthquake–induced landslide hazards. Traditionally, the Newmark critical acceleration model has commonly been used to evaluate the potential earthquake–induced landslide hazard. This method needs to assume the failure depth of the slope, which leads to an underestimation of the predicted displacement of the seismic landslide. Recently, the prediction equations of critical acceleration based on a parametric study of the limit equilibrium method overcomes the limitation of Newmark critical acceleration model and has been applied to assessments of co-seismic landslide hazards. In this study, we use Newmark critical acceleration model and prediction equations of critical acceleration to obtain the distribution maps of potential earthquake–induced landslide hazard in Shimian County, with peak ground acceleration of 10% and 2% exceeding the probability in 50 years. In addition, the nonlinear effect of site and topographic effects on peak ground acceleration were considered. The results show that Newmark critical acceleration model underestimates the area and value of the predicted displacement, while prediction equations of critical acceleration produces seismic landslides in a wider range of mountainous areas. This indicates that the critical acceleration model has a significant influence on assessments of potential earthquake–induced landslide hazards. In addition, the study not only provides valuable reference for assessment of potential earthquake–induced landslide hazard, emergency response of seismic landslides, and land planning in the study area, it also provides a useful demonstration for the selection of a critical acceleration model in seismic landslide hazard assessments for future researches.

Developing conceptual models for the recognition of coseismic landslides hazard for shallow crustal and megathrust earthquakes in different mountain environments – an example from the Chilean Andes

Landslides represent the most frequent geological hazard in mountainous environments. Most notably, landslides are a major source of fatalities and damage related to strong earthquakes. The main aim of this research is to show through three-dimensional engineer-friendly computer drawings, different mountain environments where coseismic landslides could be generated during shallow crustal and megathrust earthquakes in the Andes of central Chile. We have determined topographic, geomorphological, geological and seismic controlling factors in the occurrence of earthquake-triggered landslides from: (1) a comparison of local earthquake-induced landslide inventories in Chile (the M w 6.2, shallow crustal Aysén earthquake in 2007 (45.3° S) and the M w 8.8, megathrust Maule earthquake in 2010 (32.5°S–38.5°S)) with others from abroad; and (2) analysis of large, prehistoric landslide inventories proposed as likely induced by seismic activity. With these results, we have built four representative geomodels of coseismic landslide geomorphological environments in the Andes of central Chile. Each one represents the possible landslide types that could be generated by a shallow crustal earthquake v. those likely to be generated by a megathrust earthquake. Additionally, the associated hazards and suggested mitigation measures are expressed in each scenario. These geomodels are a powerful tool for earthquake-induced landslide hazard assessment. Thematic collection: This article is part of the Ground models in engineering geology and hydrogeology collection available at: https://www.lyellcollection.org/cc/Ground-models-in-engineering-geology-and-hydrogeology

Seismic Response of a Mountain Ridge Prone to Landsliding

ABSTRACTDuring an earthquake, site effects can play an important role in triggering landslides. To document the seismic response of steep hillslopes, we deployed broadband seismometers across a mountain ridge in Taiwan, in an area with a high earthquake-induced landslide hazard. The ridge has a simple, representative shape, and landslides have previously occurred there. Our seismometer array has recorded continuously during more than 1 yr, with both ambient-noise and regional moderate earthquakes as sources. Processing horizontal and vertical signal components, we show that the ridge has a complex response, which we attribute to the combined effects of the subsurface geology and the topographic geometry. Amplification and directionality of ground motion are observed both high and low on the ridge, giving rise to localized, elevated, earthquake-induced landslide hazard. Our database contains earthquakes with mostly similar locations, making it difficult to determine the effect of earthquake back azimuth on the ridge response. A part of the ridge response, possibly due to topographic effects, seems to be explained by a model derived from a frequency scale curvature proxy at low frequency. If correct, this would be a promising first step toward improving local ground-motion estimation in mountain areas. However, the definition of appropriate scaling parameters of site effects based on geophysical measurements, for use in regional and global landslide hazard equations applicable to mountain areas with substantial regolith thickness, remains a significant challenge.

Obtaining suitable logic-tree weights for probabilistic earthquake-induced landslide hazard analyses

In this study, an inventory of landslides induced by the 2011 Lorca earthquake (Mw 5.1) has been used in order to develop a new procedure to obtain objective logic-tree weights for a probabilistic earthquake-induced landslide hazard analysis. The 2011 Lorca earthquake triggered more than 250 landslides, mainly of disrupted type. The logic-tree was designed having regard to variability of relevant geotechnical parameters involved in the problem and uncertainties associated with the use of several empirical relationships in order to compute Newmark displacements. For the purpose, the resulting hazard maps were compared with this landslide inventory, and weights estimated for each branch of the logic tree based on these results. The best model for seismic landslide hazard mapping for a moderate earthquake correctly identifies around 72% of landslide areas. Based on the set of parameters that comprises (depth of failure surface, specific weight, cohesion, friction angle and Newmark displacement model), the corresponding weights were objectively established. These weights are reliable enough for the obtaining seismic landslide hazard maps and may be implemented in similar environments characterized by moderate-low magnitude earthquakes (Mw < 5.5).

Unbiased logic-tree data for earthquake-induced landslide hazard maps for low-to-moderate magnitude events.

Land-use planning in regard of earthquake-triggered landslides is usually implemented by means of the production of hazard maps. The well-known Newmark rigid block methodology is the most frequent used approach for this purpose. In this method, slope stability is evaluated by the estimation of the Newmark displacement, which is used to set different categories of hazard. This methodology presents limitations due to the difficulty of incorporating the variability of the used variables. For that reason, the logic-tree approach has been used in order to incorporate the epistemic uncertainties and compute probabilistic seismic-landslide hazard maps. However, the used weights in the logic-tree are usually set for each branch based on an expert judgement or subjective criteria. This article provide data obtained from the use of logic-tree methodology; this dataset is useful for deriving the unbiased weights to use in such methodology and in moderate-to-low magnitude scenarios. The data presented here are related to the article entitled “Obtaining suitable logic-tree weights for probabilistic earthquake-induced landslide hazard analyses” (Rodríguez-Peces et al., 2020) [1].

Hazard-Consistent Earthquake Scenario Selection for Seismic Slope Stability Assessment

Design ground shaking intensity, based on probabilistic seismic hazard analysis (PSHA) maps, is most commonly used as a triggering condition to analyze slope stability under seismic loading. Uncertainties that are associated with expected ground motion levels are often ignored. This study considers an improved, fully probabilistic approach for earthquake scenario selection. The given method suggests the determination of the occurrence probability of various ground motion levels and the probability of landsliding for these ground motion parameters, giving the total probability of slope failure under seismic loading in a certain time interval. The occurrence hazard deaggregation technique is proposed for the selection of the ground shaking level, as well as the magnitude and source-to-site distance of a design earthquake, as these factors most probably trigger slope failure within the time interval of interest. An example application of the approach is provided for a slope near the highway in the south of Sakhalin Island (Russia). The total probability of earthquake-induced slope failure in the next 50 years was computed to be in the order of 16%. The scenario peak ground acceleration value estimated from the disaggregated earthquake-induced landslide hazard is 0.15g, while the 475-year seismic hazard curve predicts 0.3g. The case study highlights the significant difference between ground shaking scenario levels in terms of the 475-year seismic hazard map and the considered fully probabilistic approach.

Relationship between earthquake-induced excess pore water pressure and strong ground motion observed in a monitored fill slope

The generation of excess pore water pressures during ground shaking can be an important earthquake-induced landslide trigger. We measured pore water pressures and seismic ground motion in filled slopes and examined their relationships obtained from 29 observed earthquakes. We characterized pore water pressure and seismic waveforms by calculating several parameters that described their magnitudes and durations. We demonstrate that whilst the instantaneous magnitude of strong ground motion is responsible for the generation of excess pore water pressure, the magnitude of excess pore water pressure generated is related to the peak ground acceleration (PGA) and Arias Intensity (AI) in the observed PGA range of 0.12–1.9 m/s2. The magnitude of peak ground velocity (PGV) also produced a strong correlation although a few exceptional events where long period component (>1 s) of seismic waves is dominant were observed. Lastly, the time-span over which excess pore water pressure continues to rise is affected by the duration of seismic ground motion. Our study shows how excess pore water pressures can be generated in fill-slopes during seismic events. This new knowledge could be used to improve future earthquake-induced landslide hazard assessment.

An approach of preparing earthquake induced landslide hazard map: a case study of Nuwakot District, central Nepal

The Gorkha Earthquake-2015 triggered landslides which are widespread in central Nepal. The landslides swept away physical infrastructures like roads, schools, public and residential buildings, and cultivated lands at several locations. This indicated that the decision makers were not aware of the fact that the locations for possible earthquake-induced landslides can be predicted, and physical infrastructure development can be planned accordingly. What is needed for the purpose is an earthquake-induced landslide hazard map which is a useful tool in decision making, particularly for finding safer geographical locations for residential and public building construction, and also for other physical infrastructure development. Immediately after the Gorkha Earthquake-2015, JICA prepared an earthquake-induced landslide hazard map of the Gorkha and the Sindhupalchowak Districts using a certain methodology. But there remains a research question regarding whether the same methodology can be applied in preparing earthquake-induced landslide hazard maps of other earthquake-affected districts located away from the epicenter area. The main purpose of this research was to apply the JICA methodology to prepare an earthquake-induced landslide hazard map of the Nuwakot District, central Nepal which is the one if the most affected district by Gorkha earthquake 2015. The second purpose was to examine whether the map captured the ground reality or not. While preparing the input data required, four major disaster factors were taken into consideration which includes, among others, slope inclination, slope direction, relationship with the major thrust and distance from the epicenter. These factors were classified and characterized according to their nature and condition. The result was then analyzed by using quantification theory. An earthquake-induced landslide hazard map was then prepared using QGIS as a major software tool. The map was also verified through ground-truthing visiting several locations of the study site. The proposed methodology can be used to prepare similar maps in other affected districts of Gorkha earthquake 2015, and suitable sites for constructing physical infrastructures like roads, residential and public buildings can also be identified using the maps.

Earthquake-Induced Landslide Hazard Assessment of Chamoli District, Uttarakhand Using Relative Frequency Ratio Method

Landslides are a natural hazard that affects at least 15% of the land area of India (> 0.49 million km2). Landslide induced due to earthquake is one of the most prevalent seismic hazards, which claims hundreds of lives in the Himalayan hilly terrains of India. Uttarakhand state is among the most seismically active parts of India. The objective of this study is to prepare an earthquake-induced landslide hazard map for the Chamoli district, Uttarakhand. Then, compare and validate with existing landslide database. Relative frequency ratio (RFR) method is used to identify the correlation between landslide occurrence and landslide controlling factors. Landslide hazard index (LHI) is calculated by the weighted linear combination of weight of factors. A total five factors, viz. slope angle, slope aspect, slope curvature, normalized difference vegetation index (NDVI) and PGA, are used as input geo-factors. CartoDEM and Landsat 8 TM images are considered as basic data source for generating these layers. To assess the earthquake-induced landslide in the considered region, these factors along with landslide inventory are used to calculate ratings and weights. The rating maps were integrated in geographic information system (GIS) to obtain the landslide hazard map. A final landslide hazard map showing five categories, namely very low hazard (VLH), low hazard (LH), moderate hazard (MH), high hazard (HH) and very high hazard (VHH), is presented. The results are important for regional land use planning and disaster risk reduction strategies. The hazard map provides useful information for hazard mitigation and to minimize the impact of earthquake for landslide and efficient planning for evacuation during the disaster. It is the first comprehensive attempt to produce the earthquake-induced landslide hazard map of complete district based on the earthquake-induced landslide inventory using relative frequency ratio method.

Probabilistic assessment of earthquake-induced landslide hazard including the effects of ground motion directionality

Earthquake ground motions can vary in different horizontal directions. These directional variations need to be included in the earthquake-induced landslide hazard assessment. This paper examines the influence of ground motion directionality on the probabilistic seismic displacement hazard of slopes. The distribution of the orientation of the maximum sliding displacement direction are analyzed, and the optimal ground motion parameters are provided by which the displacement in the orientation of maximum ground motion parameters can be used to represent the maximum sliding displacement over all orientations. Predictive relationships of orientation-independent sliding displacements are developed for different types of ground motions (i.e., forward-directivity pulse and non-pulse ground motions). The proposed relationships for sliding displacement are based on ground motion parameters for which there are available ground motion prediction equations (GMPEs), hence allowing for fully compatible hazard analyses with existing GMPEs. Probabilistic assessment of earthquake-induced landslide hazard is performed for hypothetical slopes, and the seismic landslide hazard maps and the ground-motion selection procedure for site-specific analysis of slopes are presented by including the effects of ground motion directionality in different types of ground motions.

An integrated approach to earthquake-induced landslide hazard zoning based on probabilistic seismic scenario for Phlegrean Islands (Ischia, Procida and Vivara), Italy

In this study we present an integrated approach to assess earthquake-induced landslide hazard at the source area of the slope instability process. The method has been applied to the case study of Ischia, Procida and Vivara islands that represent an integral part of the Campi Flegrei, a densely populated, active volcanic area, located at the NW margin of the Naples Bay, Italy.The proposed method follows a stepwise procedure including: 1) Probabilistic Seismic Hazard Analysis (PSHA); 2) assessment of site and topographic effects; 3) input of the PSHA outputs into a classic sliding rigid-block analysis for slope instability (Newmark's approach); 4) construction of landslide frequency - magnitude curves for the estimate of the slope failure probability as a function of defined Newmark's threshold values under different probabilistic seismic scenarios; 5) construction of earthquake-induced landslide hazard maps at the source area, based on the integration of the probabilistic approach and the geological, morphological and geotechnical database available for the study area.The Probabilistic Seismic Hazard Analysis (PSHA) is aimed at the definition of the seismic input with different annual exceedance frequency. PSHA results, expressed in terms of Peak Ground Acceleration (PGA) at the bedrock, are calculated for 14 return periods (T) ranging from 10 to 2000yr. PGA values have been corrected for the site effect associated with geological and morphologic conditions for each selected return period.Secondly, the corrected PGA values have been used as an input for the classic sliding rigid-block Newmark's approach, implemented in a Geographic Information System (GIS) to assess the relative potential for slope failure (landslide susceptibility) both in static (Factor of Safety, FS) and dynamic (Critical acceleration, ac) conditions. The combination of T-dependent, site-corrected PGA with the critical acceleration allowed for the calculation of the expected Newmark's displacements (DN) under different probability of exceeding or return periods (probabilistic seismic scenarios).As a further step, in order to estimate the earthquake-induced landslide hazard, we defined three DN threshold values that have considered capable to trigger shallow seismic-induced landslides in the regional context, and mapped the sectors with DN values exceeding such thresholds. On this basis, we constructed frequency-magnitude curves to estimate the probability of slope failures at the source areas, as a function of DN, by correlating the annual probability of landslide occurrence with the number of terrain cells associated with DN values greater than the selected threshold.Finally, based on the estimated annual landslide frequency of the seismic triggering event for each terrain cell, we implemented a 1:5000 scale map of Earthquake-induced Landslide Hazard for Ischia, Procida and Vivara Islands. The map reports the zoning and ranking of study area into sub-zones, on a pixel basis, according to the degree of the potential hazard from landslides derived by the frequency of the triggering event.

Probabilistic assessment of the vulnerability of reinforced concrete buildings subjected to earthquake induced landslides

This study aims at the proposition of a probabilistic framework for the vulnerability assessment of reinforced concrete (RC) buildings subjected to seismically induced slope displacements due to landslide hazard. The proposed approach combines the seismic hazard analysis with the displacement hazard analysis and the fragility analysis of the structure. First, a probabilistic seismic hazard curve for the selected intensity measure(s) is derived together with the associated information for magnitude and magnitude/distance disaggregation based on a traditional probabilistic seismic hazard analysis (PSHA). Then, a displacement hazard curve is obtained based on the associated seismic hazard analysis results and appropriate scalar or vector models to predict the seismically induced slope displacements. Finally, a damage hazard curve of a typical RC frame structure subjected to the seismically induced landslide hazard is computed describing the annual rate of exceeding different damage limit states. Various sources of uncertainty are taken into account including the aleatory variability on the seismic and displacement hazard, the structural demand and the definition of damage limit states as well as the structure’s capacity. To incorporate epistemic uncertainties in the proposed framework, a logic tree analysis is proposed to derive a weighted mean and different fractiles damage hazard curves. The proposed approach is illustrated on a hypothetical low-rise RC frame structure impacted by the earthquake induced landslide hazard. The importance of the yield coefficients and displacement predictive models to the slope’s performance, structural response and vulnerability is highlighted.