Impact Of Landslides Research Articles

The impact of environmental change on landslides, fatal landslides, and their triggers in Pakistan (2003–2019)

The actual impact of landslides in Pakistan is highly underestimated and has not been addressed to its full extent. This study focuses on the impact which landslides had in the last 17 years, with focus on mortality, gender of deceased, main triggers (landslides and fatal landslides), and regional identification of the hotspots in Pakistan. Our study identified 1089 landslides (including rockfalls, rockslides, mudslides, mudflows, debris flows) out of which 180 landslides were fatal and claimed lives of 1072 people. We found that rain (rainfall and heavy rainfall)-related landslides were the deadliest over the entire study period. The main trigger of landslides in Pakistan is heavy rainfall which comprises over 50% of the triggers for the landslide, and combined with normal rainfall, this rate climbs to over 63%. The second main reason for landslide occurrence is spontaneous (due to rock instability, erosion, climate change, and other geological elements) with landslides accounting for 22.3% of all the landslides. Landslides caused by rain-related events amounted to 41.67% of the fatalities, whereas spontaneous landslides caused 29.44% of the deaths and the human induced events accounted for 25.5% of the fatalities. The fatal landslides accounted for 19.53% deaths of the children. Our study also found that more than 48% of the deadly landslides occurred between the months of January to April, whereas the least fatal landslides occurred in the month of June which accounted for only 3% of all the fatal landslides in Pakistan.

Kuwari Lake: Potentially Dangerous for Landslide Lake Outburst Flood (LLOF)

Abstract Here a report of a lake of about one km in length that resulted from damming of a river by landslide near Kuwari village at the altitude of 1733 m asl and located between N 30o 5' 16"- 30o 5' 16" and E 79o 48' 30" - 79o 48' 29". On the basis of Google Earth images, it has been observed that a huge landslide took place from 16th to 17th June, 2013 and also in 2019 (as per the local sources) that blocked the Shambhu Gad (Baura Gad) in Bageshwar district and formed a huge lake, which covered ~15666 m2 area. It was also observed that an old landslide was reactivated due to cloud burst or toe erosion and affected an area ~862913 m2 between 2664 and 1761 m asl. The area affected by the landslide is ~4 km, the maximum width is about 650 m. In the vicinity of Kuwari village the impact of this Kuwari landslide also observed in the Jhaliya village (district Chamoli) established over south facing slope in the right bank of Pinder river. Both the villages are separated by E-W trending ridge (3700 m asl).

Ecosystem Services Assessment for Their Integration in the Analysis of Landslide Risk

Landslides are disasters that cause damage to anthropic activities, innumerable loss of human life, and affect the natural ecosystem and its services globally. The landslide risk evaluated by integrating susceptibility and vulnerability maps has recently become a manner of studying sites prone to landslide events and managing these regions well. Developing countries, where the impact of landslides is frequent, need risk assessment tools to address these disasters, starting with their prevention, with free spatial data and appropriate models. However, to correctly understand their interrelationships and social affection, studying the different ecosystem services that relate to them is necessary. This study is the first that has been attempted in which an integrated application methodology of ecosystem services is used to know in a systematic way if the information that ecosystem services provide is useful for landslide risk assessment. For the integration of ecosystem services into the landslide risk evaluation, (1) eight ecosystem services were chosen and mapped to improve understanding of the spatial relationships between these services in the Guerrero State (México), and (2) areas of synergies and trade-offs were identified through a principal component analysis, to understand their influence on risk analysis better. These are extracted from the models of the ARIES platform, artificial intelligence, and big data platform. Finally, (3) the similarity between the risk characteristics (susceptibility and vulnerability, already mapped by the authors) and the ecosystem services assessment was analysed. The results showed that the ecosystem services that most affect the synergy are organic carbon mass and the potential value of outdoor recreation; meanwhile, the possible removed soil mass was the most important trade-off. Furthermore, the lowest similarity value was found between landslide vulnerability and ecosystem services synergy, indicating the importance of including these ecosystem services as a source of valuable information in the risk analysis methodologies, especially with respect to risk vulnerability.

Dimensional effect of CFD analysis for submarine landslides interactions with infinite suspension pipelines

In this study, a computational fluid dynamics method is employed to numerically investigate the impact of submarine landslides with shear-thinning non-Newtonian fluids on infinite suspension pipelines with different diameters at various Reynolds numbers, revealing the influence of the dimensional effect between the small-scale model and the engineering prototype on impact force (i.e., drag and lift forces) coefficients. The study results show that the impact force coefficients increase to varying degrees with the pipeline diameter; the influence of the pipeline diameter on the lift force is significantly higher than that on the drag force; the minimum lift force coefficient for the pipeline diameter of 500 mm is 20.2 times higher than that for the pipeline diameter of 25 mm; and the small-scale model significantly underestimates the impact forces on the prototype, resulting in dangerous submarine pipeline designs. With increasing pipeline diameter, the slumping phenomenon clearly occurs in submarine landslides due to the gravitational potential energy and it causes a rapid increase in the head velocity of the landslide, thereby significantly increasing the impact force coefficients, especially for pipelines with large diameters. Furthermore, in gravity-free simulations, the impact force coefficients decrease with increasing pipeline diameter. This is because when the impact velocity is large and the pipeline diameter is small, submarine landslides have a high shear rate on the pipeline, resulting in high frictional stresses. Notably, the influence of dimensional effects needs to be considered to determine the velocity and shear rate of the submarine landslides around/on pipelines.

Hybrid machine learning approach for landslide prediction, Uttarakhand, India

Natural disasters always have a damaging effect on our way of life. Landslides cause serious damage to both human and natural resources around the world. In this paper, the prediction accuracy of five hybrid models for landslide occurrence in the Uttarkashi, Uttarakhand (India) was evaluated and compared. In this approach, the Rough Set theory coupled with five different models namely Bayesian Network (HBNRS), Backpropagation Neural Network (HBPNNRS), Bagging (HBRS), XGBoost (HXGBRS), and Random Forest (HRFRS) were taken into account. The database for the models development was prepared using fifteen conditioning factors that had 373 landslide and 181 non-landslide locations that were then randomly divided into training and testing locations with a ratio of 75%:25%. The appropriateness and predictability of these conditioning factors were assessed using the multi-collinearity test and the least absolute shrinkage and selection operator approach. The accuracy, sensitivity, specificity, precision, and F-Measures, and the area under the curve (AUC)-receiver operating characteristics curve, were used to evaluate and compare the performance of the individual and hybrid created models. The findings indicate that the constructed hybrid model HXGBRS (AUC = 0.937, Precision = 0.946, F1-score = 0.926 and Accuracy = 89.92%) is the most accurate model for predicting landslides when compared to other models (HBPNNRS, HBNRS, HBRS, and HRFRS). Importantly, when the fusion is performed with the rough set method, the prediction capability of each model is improved. Simultaneously, the HXGBRS model proposed shows superior stability and can effectively avoid overfitting. After the core modules were developed, the user-friendly platform was designed as an integrated GIS environment using dynamic maps for effective landslide prediction in large prone areas. Users can predict the probability of landslide occurrence for selected region by changing the values of a conditioning factors. The created approach could be beneficial for predicting the impact of landslides on slopes and tracking landslides along national routes.

Landsifier v1.0: a Python library to estimate likely triggers of mapped landslides

Abstract. Landslide hazard models aim at mitigating landslide impact by providing probabilistic forecasting, and the accuracy of these models hinges on landslide databases for model training and testing. Landslide databases at times lack information on the underlying triggering mechanism, making these inventories almost unusable in hazard models. We developed a Python-based unique library, Landsifier, that contains three different machine-Learning frameworks for assessing the likely triggering mechanisms of individual landslides or entire inventories based on landslide geometry. Two of these methods only use the 2D landslide planforms, and the third utilizes the 3D shape of landslides relying on an underlying digital elevation model (DEM). The base method extracts geometric properties of landslide polygons as a feature space for the shallow learner – random forest (RF). An alternative method relies on landslide planform images as an input for the deep learning algorithm – convolutional neural network (CNN). The last framework extracts topological properties of 3D landslides through topological data analysis (TDA) and then feeds these properties as a feature space to the random forest classifier. We tested all three interchangeable methods on several inventories with known triggers spread over the Japanese archipelago. To demonstrate the effectiveness of developed methods, we used two testing configurations. The first configuration merges all the available data for the k-fold cross-validation, whereas the second configuration excludes one inventory during the training phase to use as the sole testing inventory. Our geometric-feature-based method performs satisfactorily, with classification accuracies varying between 67 % and 92 %. We have introduced a more straightforward but data-intensive CNN alternative, as it inputs only landslide images without manual feature selection. CNN eases the scripting process without losing classification accuracy. Using topological features from 3D landslides (extracted through TDA) in the RF classifier improves classification accuracy by 12 % on average. TDA also requires less training data. However, the landscape autocorrelation could easily bias TDA-based classification. Finally, we implemented the three methods on an inventory without any triggering information to showcase a real-world application.

Progressive collapse fragility of substandard and earthquake-resistant precast RC buildings

Several natural and man-made disasters have highlighted that precast reinforced concrete (RC) buildings often have insufficient levels of structural robustness, which is the last line of defence under extreme events. Even though robustness studies were originated by the progressive collapse of the Ronan Point precast RC building in 1968, such a structural feature of disaster resilience has been partially investigated in case of precast RC buildings to date. In this study, a precast RC frame structure representative of low-rise commercial buildings is analysed under different column loss scenarios, which can produce the partial or total collapse of the structural system. Two building classes are considered, namely, buildings designed only to gravity loads and buildings designed for earthquake resistance. Structural robustness is probabilistically assessed through a fragility analysis procedure, using three-dimensional fibre-based finite element models with nonlinear links simulating connections, large-displacement incremental dynamic analysis, and multiple performance limit states for damage assessment. The output of the study is threefold: (i) a detailed assessment of the effects of column loss on precast RC frame buildings, quantifying the major role of structural detailing and beam-column connections; (ii) a fragility-informed evaluation of beneficial effects of seismic detailing on structural robustness, which can drive designers in retrofitting of existing precast RC buildings and decision-makers towards prioritization-related issues; and (iii) the generation of typological fragility curves for progressive collapse risk assessment in both single- and multi-hazard environments. Progressive collapse fragility curves can be convolved with more classical fragility models describing the failure of single components (under, e.g., flow-type landslide impact, vehicle collision or blast) and hazard, to assess systemic structural risk.

Effects of Landslide on the Livelihood of People at Ghumthang, Sindhupalchok

Landslides are considered as one of the most recurring and common disaster in Nepal. They cause huge loss to infrastructure, property and human casualty. The intensity of landslides in Nepal’s mid hills are rising as one of the most common and devastating disasters. The 2020 Ghumthang landslide in Nepal’s Sindhupalchok District destroyed the houses, lands and infrastructures and impacted livelihoods of the people living in the area. Every monsoon landslides in hilly region of the country lead to impact on the livelihood of the people and this aspect has not been analyzed in depth. The disaster-affected people face the destruction crop, livestock, and land. They also face damages of house, drinking water and other infrastructure,. In addition they face non-economic effects like health and mental stress. This article based on a household survey combines quantitative and qualitative data from 50 respondents. Questionnaire, interviews and focus group discussions were held during field survey to solicit their persctives Secondary sources were consulted and lived experience used to triangulate the survey findings for accuracy. This study reveals multidimensional nature of the impact of landslide on the life of people who live in the affected area.

Landslide Susceptibility Assessment in Mojokerto Regency Using Logistic Regression

The impact of landslides varies from place to place, including cutting off transportation routes, destroying agricultural land, and/or destroying houses. Due to the high threat of landslides, it is necessary to make efforts to improve community preparedness by disseminating information about landslide distribution. In this research, landslide assessment was conducted using logistic regression. Twelve landslide factors were assessed including topographic position index, stream power index, slope, aspect, elevation, profile curvature, distance to drainage, soil, rainfall, land use, and distance to road. The assessment of the landslide susceptibility level in this study was highly accurate, based on the AUC value obtained, which was 0.92. The results of the assessment of the landslide susceptibility level were divided into five classes with the following areas: very low 36%, low 4.4%, moderate 2.91%, high 4.1% and very high 52.5%.
 Keywords: scientific, approach, methodological, techniques, geography

Deterministic and Probabilistic Seismic Hazard Analysis of Tindharia, Darjeeling Sikkim Himalaya, India

Abstract The simulation of appropriate ground motion is the main objective in earthquake-resistant designing to mitigate the impact of landslides and structural damages. The seismic hazard studies serve as the basis for quantifying the hazard associated within the particular area over a given time period. In this study, the detailed site specific hazard analysis of Tindharia site as well as Darjeeling Sikkim Himalaya (DSH) region has been carried out to assure the safety of human and infrastructure against seismic risk. The study area is mountainous region on north western side of India, falls in seismic zone IV and V, and is one of the most seismically active regions with vulnerable earthquakes along with fatal landslides. The future large earthquakes are likely to occur in the region due to constant movement of Indian plate towards Eurasian plate. Therefore, the heightened seismic risk increases the demand for seismic studies near the Himalayan belt. Thus, the assessment of associated seismic hazard in the study area has been carried out by deterministic and probabilistic seismic hazard approaches. From the results, the seismo-tectonic map of a 300km radial distance of the study area is developed. The deterministic hazard distribution in terms of peak ground acceleration (PGA) and the PGA and peak spectral acceleration (PSA) hazard maps for 2% and 10% probability of exceedance in 50 years at different time periods have been generated. The updated hazard maps developed in this study play pivotal roles in mitigation measures with better pre-disaster prevention by earthquake resistant design and in preparedness and post disaster rescue in the study area.

ANALISIS KERENTANAN TANAH LONGSOR DI SUB DAS CEMORO DENGAN APLIKASI SISTEM INFORMASI GEOGRAFIS

Landslides in Bengawan Solo Watershed can be caused by the increasing area of critical land and continuous process of erosion. Landslides vulnerability also caused by the condition of slope that are sloping to steep, soil textures which tends to smooth and deep regolith. It can affect the activity of the community around the watershed. This study aims to analyze the level of landslides vulnerability in Cemoro sub-watershed as part of the Bengawan Solo Watershed in order to reduce the impact of landslides by using Geographic Information System. Landslides vulnerability is determined by considering factors such as geological type, soil regolith depth, slope, fault and soil texture. Based on the result of landslides vulnerability analysis, the Cemoro sub watershed has two levels of landslides vulnerability which are a low level of 68,17% with 658,4 ha of area and a medium level of 31,83% with 307,35 ha of area. Results of this study is a map of landslides vulnerability which is used as a reference to improve mitigation in the Cemoro sub watershed using soil conservation.

Seismic and geomorphic assessment for coseismic landslides zonation in tropical volcanic contexts

The Poás volcano is an active volcano of Costa Rica with intense tectonic activity in its flanks. Historically, the volcano has presented strong, surficial earthquakes provoking many landslides with associated casualties and immense economic impacts. One example is the Cinchona earthquake in 2009 (Mw 6.2 and 4.6 km depth). We aim to determine a landslide zonation according to seismic data and a geomorphic assessment in the NW sector of the Poás volcano based on a combination of qualitative methods and morphometric parameters. The results estimate the possible outcome of a 6.8 Mw earthquake. The susceptibility mapping and models showed a positive relationship between this methodology and others previously developed for the area that surrounds the Poás volcano as well as a correspondence with the landslides caused by the Cinchona earthquake in 2009. The final coseismic landslides susceptibility zoning indicated that 52% of the area is prone to landslides. Furthermore, there is a relationship between the areas with high exposure to landslides with conical volcanic geomorphologies, active faulting structural and/or tectonic geomorphological units with slopes greater than 15°. The proposed zoning can be useful for land use planning and by civil protection entities to orientate quick response and reduce the impact of future landslides. Moreover, this method can be applied in active tectonic and volcanic areas worldwide.

Analytical and numerical models of debris flow impact

A full understanding of the interaction mechanisms among flow-like landslides and impacted protection structures is still an open issue. Although several approaches, from experimental to numerical, have been used so far, a thoroughly assessment of the hydromechanical behaviour of the landslide body is achievable only through a multiphase and large deformation approach.This paper firstly proposes a conceptual model for a specific type of protection structure, namely a Deformable Geosynthetics-Reinforced Barrier (DGRB), i.e., an embankment made of coarse-grained soil layers reinforced by geogrids. In such a case, the sliding of the barrier along its base, under the impulsive action of a flow-type landslide, is an important landslide energy dissipation mechanism, and a key issue for the design. Then, two different approaches are proposed: i) an advanced hydro-mechanical numerical model based on Material Point Method is tested in simulating the whole complex landslide-structure interaction mechanisms, ii) an analytical model is set up to deal with the landslide energy dissipation and the kinematics of both the landslide and barrier. The calibration of the proposed analytical model is pursued based on the numerical results. Finally, the analytical model is successfully validated to interpret a large dataset of landslide impact field evidence, for whose interpretation also five empirical methods available in the literature are tested.

Effect of opening and wall boundaries on CFD modeling for submarine landslide-ambient water-pipeline interaction

Submarine landslide-ambient water-pipeline interactions are receiving extensive attention in ocean engineering, especially the impact forces caused by landslides on pipelines. Computational fluid dynamics (CFD) approaches are widely used to obtain these forces; however, as necessary definite solution conditions, the boundary conditions of CFD modeling are challenging to reasonably determine. In this study, the relationship between the finite volume method (FVM) in CFD modeling and boundary conditions is presented, and a classic physical flume experiment is reproduced using CFD modeling, verifying the effectiveness of the established modeling. The quantitative effect of the opening and free slip wall boundaries under variable complex working conditions (i.e., Reynolds number, pipeline span height, landslide impact velocity, and landslide rheological model) is explored via a general geometry model. The evolution characteristics of impact forces on pipelines under different boundary conditions are given. The mechanism caused by impact force changes is explained through the distributions of the velocity field, pressure field and streamlines around pipelines. Furthermore, a method for determining boundary conditions is briefly proposed, providing a basis for optimizing the CFD modeling of submarine landslide-ambient water-pipeline interactions.

Application of Scenario Earthquakes for Analysis of Seismically Triggered Landslide Hazard: A Case Study in Costa Rica

In this study, we demonstrate the capabilities of hypothetical scenario earthquakes as a new tool for assessment of hazards associated with earthquake-triggered landslides. Costa Rica offers an ideal environment for demonstrating the utility of scenario earthquakes due to its diverse tectonic environments and associated widespread seismic hazard, rugged topography, and high landslide susceptibility. We investigate the relative influence of landslide proxies such as topographic slope, peak ground velocity (PGV), and compound topographic index (CTI), and earthquake source parameters such as magnitude and depth, on predicted landslide probability and fatality. We examine five distinct tectonic environments, including subduction events beneath the (1) Nicoya and (2) Osa peninsulas respectively, (3) intraplate earthquakes beneath the Central Volcanic Range (CVR) and (4) the Central Costa Rica Deformed Belt (CCRDB), and (5) back-arc thrust events on the eastern Caribbean coast. Our results demonstrate that the slope, PGV, and CTI thresholds necessary to produce landslide probabilities greater than 10% vary by tectonic environment. In all cases, we observe magnitude to be the primary control on the predicted maximum landslide probability and overall areal landslide coverage. We validate model predictions with observed landslide inventories from the 2009 Cinchona and 1991 Limon earthquakes, demonstrating a good fit, where over 70% of landslides occurring in zones of greater than 20% probability. We also use a global model of landslide impact to predict exposure and fatality ranges for each scenario earthquake of this study, revealing that moderate-sized earthquakes in the CCRDB and CVR and large subduction megathrust earthquakes each pose a significant hazard to Costa Rica’s population.

Numerical simulation of landslide-generated waves using a SPH-DEM coupling model

Landslide-generated waves are natural disasters that have raised the attention and concerns of researchers and engineers during the past decades. Since landslide-generated waves involve complex processes such as the impact of landslides, wave generation, and propagation, traditional numerical methods cannot capture this process well. Based on Darcy's law, this study proposed a coupled algorithm of smooth particle hydrodynamics (SPH) and discrete element method (DEM) to deal with the interaction between particles and fluids at the macroscopic scale. The interrelationship between fluid permeation velocity and pressure in solids is considered in the coupled algorithm. The FORTRAN language established the landslide-generated surge wave model under the SPH-DEM fluid-solid coupling framework. The surge waves generated by underwater submerged deformable landslides and the high-speed unsubmerged deformable landslides were simulated as typical landslide cases in this study. Meanwhile, the effectiveness of the coupling method is verified by comparison with experimental data. The simulation results of the current model agree well with the experimental data or simulation results in the previous literature. Therefore, the SPH-DEM model can forecast the impacts of landslides-generated waves.

Dendrochronological record of soil creep and landslide activity – the comparison of tree-ring eccentricity and compression wood (examples from the Kamienne Mts., Poland)

Forested mountain slopes can be simultaneously affected by soil creep and landslide activity, both of which cause the tilting of tree stems, with the result that their dendrochronological record of tree-ring eccentricity and compression wood is potentially similar. There is a need to identify similarities and differences in these records and thus our research aimed to compare patterns of eccentricity and compression wood developed by trees under the impact of soil creep and landslides. We sampled trees growing on a landslide and creeping slopes in the Kamienne Mts., with 21 Norway spruce trees were sampled on each site. We found several main differences between the dendrochronological record of landslide activity and soil creep. On the landslide we found larger number of dendrochronological events, stronger and more variable eccentricity and a similar number of upslope and downslope events. On creeping slopes, upslope eccentricity events predominate, and the number of eccentricity events dated in all trees increases in time. We also compared the utility of eccentricity and compression wood for dating mass movements. They differ in their sensitivity to stem tilting. Thus, in analyses of landslide activity and soil creep activity, it is recommended to include both wood anatomy features.

Ecosystem carbon stock loss after a mega earthquake

Earthquake-induced geohazards, in particular landslides and debris flows, have the ability to disturb large stocks of organic carbon. Even a slight change in these stocks of organic carbon could affect atmospheric CO2 concentrations. Yet, the fate of ecosystem carbon delivery caused by a mega-earthquake over a short timescale is not well understood. In this study, a methodology is proposed to study the impact of earthquake-triggered landslides and post-seismic debris flows on ecosystem carbon (C). Pairs-plots were established on undisturbed areas, coseismic landslides or debris flow in one of the most representative catchments, in the 2008 Wenchuan earthquake hit regions. Results showed that coseismic landslides greatly reduced vegetation C stock from 34.48 Mgha-1 to 6.85 Mgha-1 and soil C stock from 96.38 to 6.77 Mgha-1, thereby leading to 89.6% reduction for total ecosystem C stock. Similarly, debris flow deposit led to a reduction of 97.8% of ecosystem C stock. Overall, it is estimated that there was a loss of 0.118×106Mg C across the study area, indicating a large amount of carbon change after the mega-earthquake. This study has an important implication for the role of mega-earthquake on regional ecosystem C storage and fills our knowledge gaps of the impact of ecosystem C cycle along with earthquake-induced geohazard chains.

Assessment of Landslide Susceptibility of the Wiśnickie Foothills Mts. (The Flysch Carpathians, Poland) Using Selected Machine Learning Algorithms

Landslides are well-known phenomena that cause significant changes to the relief of an area’s terrain, often causing damage to technical infrastructure and loss of life. One of the possible means of reducing the negative impact of landslides on people’s lives or property is to recognize areas that are prone to their occurrence. The most common approach to this problem is preparing landslide susceptibility maps. These can factor in the actual location of landslides or the causal relationship between landslides and selected environmental factors. Creating a classification of landslide-prone areas is a challenging task when landslide density is not high and the area of analysis is large. We prepared shallow 10 m × 10 m resolution landslide susceptibility maps of the Wiśnickie Foothills (Western Carpathians, Poland) using eleven different machine learning algorithms derived from the Python libraries Scikit-learn and Imbalanced-Learn. The analyzed area is characterized by a mean density of 3.4 surficial landslides (composed of soils and rocks) per km2. We also compared different approaches to imbalanced sets of data: Logistic Regression, Naive Bayes, Random Forest, AdaBoost, Bagging, ExtraTrees (Extremely Randomized Trees), Easy Ensemble, Balanced Bagging, Balanced Random Forest, RUSBoost and a hybrid model combining Random Under Sampler and Multi-layer Perceptron algorithms. The environmental factors (slope inclination and aspect, distance from rivers, lithology, soil type and permeability, groundwater table depth, profile and plan curvature, mean annual rainfall) were categorized and divided into training (70%) and testing (30%) sets. Accuracy, recall, G-mean and area under receiver operating curve (AUC) were used to validate the quality of the models. The results confirmed that algorithms based on decision tree classifiers are suitable for preparing landslide susceptibility maps. We also found that methods that generate random undersampling subsets (Easy Ensemble, Balanced Bagging, RUSBoost) and ensemble methods (Bagging, AdaBoost, Extra-Trees) both yield very similar test results to those that use full sets of data for training. Relatively high-quality results can also be obtained by integrating the Random Under Sampler algorithm with the Multi-layer Perceptron algorithm.

A probabilistic approach for performance-based assessment of highway bridges under post-earthquake induced landslides

The seismic vulnerability of reinforced concrete (RC) highway bridges in the vicinity of landslides is usually evaluated without considering the impact of sliding mass flow-type. The paper presents a probabilistic approach for assessing the performance of highway bridges under combined ground shaking and earthquake-induced flow-type landslide. This approach combines the displacement-based hazard analysis of the landslide, the modified Cloud method for considering the effect of post-earthquake induced landslide and the development of fragility curves for bridges. Firstly, the slope displacement and instability probabilities under ground shaking were obtained based on the intensity levels of the bridge site. Then, the velocity of the flow-type landslides at the bridge site was estimated by the computational fluid dynamics theory, which was adopted to compute the impact force on the bridge columns. The impact of the flow-type landslide was included in the modified Cloud analysis by utilizing Monte Carlo simulations and the logarithmic polyline-based regression. Finally, seismic fragility curves of the RC highway bridge subjected to seismic loadings and landslide impact forces were derived with the consideration of various sources of uncertainty, including the aleatory variability on the material, geometry, structural demands and capacities. The proposed approach was illustrated by a typical RC highway bridge under both the earthquake and landslide hazards. The importance of considering the seismic-induced flow-type landslides on bridge vulnerability is highlighted. •A probabilistic approach was proposed for bridges under impact of earthquake-induced landslides; •The impact of landslides was included in Cloud analysis by Monte Carlo simulations; •The logarithmic polyline-based regression was adopted for establishing EDP-IM relationship; •Various uncertainties of earthquakes and landslides were considered in fragility analysis of bridges.