A Hazard Assessment Method for Potential Earthquake-Induced Landslides – A Case Study in Huaxian County, Shaanxi Province

The hazard assessment of potential earthquake-induced landslides is an important aspect of the study of earthquake-induced landslides. In this study, we assessed the hazard of potential earthquake-induced landslides in Huaxian County with a new hazard assessment method. This method is based on probabilistic seismic hazard analysis and the Newmark cumulative displacement assessment model. The model considers a comprehensive suite of information, including the seismic activities and engineering geological conditions in the study area, and simulates the uncertainty of the intensity parameters of the engineering geological rock groups using the Monte Carlo method. Unlike previous assessment studies on ground motions with a given exceedance probability level, the hazard of earthquake-induced landslides obtained by the method presented in this study allows for the possibility of earthquake-induced landslides in different parts of the study area in the future. The assessment of the hazard of earthquake-induced landslides in this study showed good agreement with the historical distribution of earthquake-induced landslides. This indicates that the assessment properly reflects the macroscopic rules for the development of earthquake-induced landslides in the study area, and can provide a reference framework for the management of the risk of earthquakeinduced landslides and land planning.

Characteristics and failure mechanism of an ancient earthquake-induced landslide with an extremely wide distribution area

The Lamuajue landslide is located in Lamuajue village on the tight bank of the Meigu River, Sichuan Province, China. This landslide is an ancient landslide with an extremely wide distribution area, covering an area of 19 km2 with a maximum width of 5-5 km and an estimated residual volume of 3 × 108 ma. The objectives of this study were to identify the characteristics and failure mechanism of this landslide. In this study, based on field investigations, aerial photography, and profile surveys, the boundary, lithology, structure of the strata, and characteristics of the landslide deposits were determined. A gently angled weak interlayer consisting of shale was the main factor contributing to the occurrence of the Lamuajue landslide. The deposition area can be divided into three zones： zone A is an avalanche deposition area mainly composed of blocks, fragments, and debris with diameters ranging from o.i m to 3 m; zone B is a residual integrated rock mass deposition area with large blocks, boulders and ＂fake bedrock＂; and zone C is a deposition zone of limestone blocks and fragments. Three types of failure mechanism were analyzed and combined to explain the Lamuajue landslide based on the features of the accumulation area. First, a shattering-sliding mechanism caused by earthquakes in zone A. Second, a sliding mechanism along the weak intercalation caused by gravity and water in zone B. Third, a shattering-ejection mechanism generated by earthquakes in zone C. The results provide a distinctive case for the study of gigantic landslides induced by earthquakes, which is very important for understanding and assessing ancient earthquakeinduced landslides.

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

In order to prevent and mitigate disasters,it is crucial to immediately and properly assess the spatial distribution of landslide hazards in the earthquake-affected area.Currently,there are primarily two categories of assessment techniques:the physical mechanism-based method(PMBM),which considers the landslide dynamics and has the advantages of effectiveness and proactivity;the environmental factor-based method(EFBM),which integrates the environmental conditions and has high accuracy.In order to obtain the spatial distribution of landslide hazards in the affected area with near realtime and high accuracy,this study proposed to combine the PMBM based on Newmark method with EFBM to form Newmark-Information value model(N-IV),Newmark-Logic regression model(N-LR)and Newmark-Support Vector Machine model(N-SVM)for seismic landslide hazard assessment on the Ludian Mw 6.2 earthquake in Yunnan.The predicted spatial hazard distribution was compared with the actual cataloged landslide inventory,and frequency ratio(FR),and area under the curve(AUC)metrics were used to verify the model's plausibility,performance,and accuracy.According to the findings,the model's accuracy is ranked as follows:N-SVM>N-LR>N-IV>Newmark.With an AUC value of 0.937,the linked N-SVM was discovered to have the best performance.The research results indicate that the physics-environmental coupled model(PECM)exhibits accuracy gains of 46.406%(N-SVM),30.625%(N-LR),and 22.816%(N-IV)when compared to the conventional Newmark technique.It shows varied degrees of improvement from 2.577%to 12.446%when compared to the single EFBM.The study also uses the Ms 6.8 Luding earthquake to evaluate the model,showcasing its trustworthy in forecasting power and steady generalization.Since the suggested PECM in this study can adapt to complicated earthquake-induced landslides situations,it aims to serve as a reference for future research in a similar field,as well as to help with emergency planning and response in earthquakeprone regions with landslides.

Impact of earthquake-induced landslide on the habitat suitability of giant panda in Wolong,China

Massive geological landslides and unstable landslide areas were triggered by the 2008 Wenchuan earthquake. These landslides caused deaths, damaged infrastructure and threatened endanger species. This study analyzed the impact of landslides on giant pandas and their habitats from the following aspects: threatening pandas‘ lives, damaging pandas‘ habitat, influencing giant panda behavior, increasing habitat fragmentation; the final aspect, and blocking gene flow by cutting off corridors. A habitat suitability map was created by integrating the landslide factors with other traditional factors based on a logistics regression method. According to the landslide inventory map, there are 1313 landslides, 818 rock debris flows, 117 rock avalanches and 43 mud flows occurred in the study area. A correlation analysis indicated that landslides caused the pandas to migrate, and the core landslides within 1 km2 had greater influence on panda migration. These core landslides primarily occurred in mid-altitude regionscharacterized by high slopes, old geological ages, large areas and large rock mass volumes. The habitat suitability assessment results for the Wolong Natural Reserve had better prediction performance(80.9%) and demonstrated that 14.5%, 15.9%, 20.5%, 47.6% and 1.5% of the study area can be classified as very high, high, moderate, low and very low giant panda suitability areas, respectively. This study can be used to inform panda and panda habitat research, management and protection during post-quake reconstruction and recovery periods in China.

Earthquake-induced landslide displacement attenuation models and application in probabilistic seismic landslide displacement analysis

A landslide displacement (DLL) attenuation model has been developed using spectral intensity and a ratio of critical acceleration coefficient to ground acceleration coefficient. In the development of the model,a New Zealand earthquake record data set with magnitudes ranging from 5.0 to 7.2 within a source distance of 175 km is used. The model can be used to carry out deterministic landslide displacement analysis,and readily extended to carry out probabilistic seismic landslide displacement analysis. DLL attenuation models have also been developed by using earthquake source terms,such as magnitude and source distance,that account for the effects of earthquake faulttype,source type,and site conditions. Sensitivity analyses show that the predicted DLL values from the new models are close to those from the Romeo model that was developed from an Italian earthquake record data set. The proposed models are also applied to an analysis of landslide displacements in the Wenchuan earthquake,and a comparison between the predicted and the observed results shows that the proposed models are reliable,and can be confidently used in mapping landslide potential.

Long-term Activity of Earthquake-induced Landslides: A Case Study from Qionghai Lake basin, Southwest of China

Earthquake-triggered landslides are a major geological hazard in the eastern Tibetan Plateau, and have prolonged impact on earth surface processes and fluvial system. To determine how long co-seismic landslides affect basins, a massive number of landslides existing in Qionghai Lake Basin were investigated for landslide distribution characteristics and geomorphological evidences, with further comparison and analysis using historic seismic analog method. The landslides found in Qionghai Lake Basin showed clear features of seismic triggering with strongly controlled by Zemuhe fault. These landslides are still active at present. Some new slides generally occur in ancient slope failure zones causing serious secondary hazards in recent years. In this study we strengthen the idea that the landslides triggered by the 185o Xichang earthquake （Ms7.5） have long term activity and prolonged impact on the mountain disasters with a period of more than 16o years. Our results support growing evidence that coseismic landslides have a prolonged effect on secondary disasters in a basin, and invite more careful consideration of the relationship between current basin condition and landslide history for a longer period.

Risk Assessment of Disaster Chain: Experience from Wenchuan Earthquake-induced Landslides in China

This paper deals with the formative process of the Wenchuan earthquake disaster chain risk. Selected earthquake-landslides chain risk is critically evaluated by the probability of landslide displacement failure based on the Newmark's permanent-deformation model. In this context, a conceptual model of regional disaster chain risk assessment was proposed, in which the hazardformative environments sensitivity was the core factor as well as the main difference compared with single disaster risk assessment. The disaster chain risk is accumulation of primary disaster risk and the secondary disasters risks. Results derived from the Wenchuan case proved that the conceptual model was suitable for the disaster chain risk assessment, especially the sudden disaster chain. This experience would offer greater potential in application of conceptual model of disaster chain risk assessment, in the process of large-scale disaster risk governance.

Permanent displacement models of earthquake-induced landslides considering near-fault pulse-like ground motions

The permanent displacement of seismic slopes can be regarded as an effective criterion for stability estimation. This paper studied the characteristics of permanent displacements induced by velocity pulse-like ground motions and developed an empirical model to readily evaluate the stability of seismic slopes in a near-fault region. We identified 264 velocity pulse-like ground motions from the Next Generation Attenuation(NGA) database using the latest improved energy-based approach. All selected ground motions were rotated to the orientation of the strongest observed pulse for considering the directivity of the pulse effect, so that the most dangerous condition for slopes was considered. The results show the velocity pulse-like ground motions have a much more significant effect on permanent displacement of slopes than non-pulse-like ground motions. A regression model based on a function of peak ground velocity(PGV), peak ground acceleration(PGA) and critical acceleration(ac), was generated. A significant difference was found by comparing the presented model with classical models from literatures. This model can be used to evaluate the seismic slope stability considering the effects of nearfault pulse-like characteristics.

A Combined Weight of Evidence and Logistic Regression Method for Susceptibility Mapping of Earthquake-induced Landslides: A Case Study of the April 20, 2013 Lushan Earthquake, China

The M_s 7.0 Lushan earthquake triggered a huge number of landslides. Landslide susceptibility mapping is of great importance. Weight of Evidence（Wo E） and Logistic Regression（LR） methods have been widely used for LSM（Landslide Susceptibility Mapping）. However, limitations still exist. Wo E is capable of assessing the influence of different classes of each factor, but neglects the correlation between factors. LR is able to analyze the relationship among the factors while it is not capable of evaluating the influence of different classes. This paper proposes a combined method of LR and Wo E for LSM, taking advantage of their individual merits and overcoming their limitations. An inventory of 1289 landslides was used： 70% were random-selected for training and the remaining for validation. 11 landslide condition factors were employed in the model and the result was validated using Receiver Operating Characteristic（ROC） curve. The results showed that the LRWo E model had a better accuracy than the LR model, producing an area below the curve with values of 0.802 success and 0.791 predictive, higher than that of the LR model（0.715 success and 0.722 predictive）. It is therefore concluded that the combined method of Wo E and LR can provide a promising level of accuracy for earthquake-induced landslide susceptibility mapping.

Geospatial susceptibility mapping of earthquake-induced landslides in Nuweiba area, Gulf of Aqaba, Egypt

Earthquake induced landslides are one of the most severe geo-environmental hazards that cause enormous damage to infrastructure, property, and loss of life in Nuweiba area. This study developed a model for mapping the earthquake-induced landslide susceptibility in Nuweiba area in Egypt with considerations of geological, geomorphological, topographical, and seismological factors. An integrated approach of remote sensing and GIS technologies were applied for that target. Several data sources including Terra SAR-X and SPOT 5 satellite imagery, topographic maps, field data, and other geospatial resources were used to model landslide susceptibility. These data were used specifically to produce important thematic layers contributing to landslide occurrences in the region. A rating scheme was developed to assign ranks for the thematic layers and weights for their classes based on their contribution in landslide susceptibility. The ranks and weights were defined based on the knowledge from field survey and authors experiences related to the study area. The landslide susceptibility map delineates the hazard zones to three relative classes of susceptibility: high, moderate, and low. Therefore, the current approach provides a way to assess landslide hazards and serves for geo-hazard planning and prediction in Nuweiba area.

Liquefaction Susceptibility of Soils With Clay Particles from Earthquake-induced Landslides

The main reason for earthquake-induced landslides is liquefaction of soil,a process considered to occur mostly in sandy soils.Liquefaction which occurs in clayey soils has also been reported and proven in the recent liters- ture,but liquefaction in clayey soils still remains unclear and there are many questions that need to be addressed.In order to address these questions,an depth study on the liquefaction potential of clayey soils was conducted on the ba- sis of field investigation and a series of laboratory tests on the samples collected from the sliding surface of the land- slides.The liquefaction potential of the'soils was studied by means of undrained cyclic ring-shear tests.Research re- sults show that the liquefaction potential of sandy soils is higher than that of clayey soils given the same void ratio; the soil resistance to liquefaction rises with an increase in plasticity for clayey soils;relation between plasticity index and the liquefaction potential of soil can be used in practical application to estimate the liquefaction potential of

An Uncertainty Analysis of the Newmark Displacement Model for Earthquake-Induced Landslides in the Jiuzhaigou National Geopark

In this paper,we present an approach to generating probabilistic hazard maps for earthquake-induced landslides using the Newmark Displacement Model(NDM).This model takes the uncertainties associated with the slope properties(e.g.,soil shear strengths,groundwater table location)into consideration,which is coupled with the hydrological model based on geomorphological,geological,geotechnical,seismological,and rainfall data.Uncertainties and fluctuations in the input parameters of the NDM are considered by treating these quantities asβ-PERT distributions through Monte Carlo techniques,which allows probability value of the NDM to be cast into hazard maps.Additionally,incorporating Monte Carlo techniques can avoid using conservative input parameters in a deterministic approach to capture these uncertainties.Taking the 2017 Jiuzhaigou M_(w)6.5 Earthquake in Sichuan Province,Western China as an example,earthquake-induced landslides probability distribution map is generated with the most appropriate displacement threshold(λ=1 cm).Our results show good performances for realistic landslide hazard assessment,which can serve as a basis for providing a reference for the prediction of earthquake-induced landslide probability and rapid landslide hazard assessment after a strong earthquake.

Landslide Susceptibility Mapping Using RBFN-Based Ensemble Machine Learning Models

This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.

Large-deformation finite-element modeling of seismic landslide runout: 3D probabilistic analysis with cross-correlated random field

Landslides significantly threaten lives and infrastructure, especially in seismically active regions. This study conducts a probabilistic analysis of seismic landslide runout behavior, leveraging a large-deformation finite-element (LDFE) model that accounts for the three-dimensional (3D) spatial variability and cross-correlation in soil strength — a reflection of natural soils' inherent properties. LDFE model results are validated by comparing them against previous studies, followed by an examination of the effects of univariable, uncorrelated bivariable, and cross-correlated bivariable random fields on landslide runout behavior. The study's findings reveal that integrating variability in both friction angle and cohesion within uncorrelated bivariable random fields markedly influences runout distances when compared with univariable random fields. Moreover, the cross-correlation of soil cohesion and friction angle dramatically affects runout behavior, with positive correlations enlarging and negative correlations reducing runout distances. Transitioning from two-dimensional (2D) to 3D analyses, a more realistic representation of sliding surface, landslide velocity, runout distance and final deposit morphology is achieved. The study highlights that 2D random analyses substantially underestimate the mean value and overestimate the variability of runout distance, underscoring the importance of 3D modeling in accurately predicting landslide behavior. Overall, this work emphasizes the essential role of understanding 3D cross-correlation in soil strength for landslide hazard assessment and mitigation strategies.

Dynamic intelligent prediction approach for landslide displacement based on biological growth models and CNN-LSTM

Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Regression models and Neural network models,to perform multi-characteristic coupled displacement prediction because they fail to consider landslide creep characteristics.This paper integrates the creep characteristics of landslides with non-linear intelligent algorithms and proposes a dynamic intelligent landslide displacement prediction method based on a combination of the Biological Growth model(BG),Convolutional Neural Network(CNN),and Long ShortTerm Memory Network(LSTM).This prediction approach improves three different biological growth models,thereby effectively extracting landslide creep characteristic parameters.Simultaneously,it integrates external factors(rainfall and reservoir water level)to construct an internal and external comprehensive dataset for data augmentation,which is input into the improved CNN-LSTM model.Thereafter,harnessing the robust feature extraction capabilities and spatial translation invariance of CNN,the model autonomously captures short-term local fluctuation characteristics of landslide displacement,and combines LSTM's efficient handling of long-term nonlinear temporal data to improve prediction performance.An evaluation of the Liangshuijing landslide in the Three Gorges Reservoir Area indicates that BG-CNN-LSTM exhibits high prediction accuracy,excellent generalization capabilities when dealing with various types of landslides.The research provides an innovative approach to achieving the whole-process,realtime,high-precision displacement predictions for multicharacteristic coupled landslides.

Landslide model tests with a miniature 2D principal stress sensor

Understanding the stress distribution derived from monitoring the principal stress(PS)in slopes is of great importance.In this study,a miniature sensor for quantifying the two-dimensional(2D)PS in landslide model tests is proposed.The fundamental principle and design of the sensor are demonstrated.The sensor comprises three earth pressure gages and one gyroscope,with the utilization of three-dimensional(3D)printing technology.The difficulties of installation location during model preparation and sensor rotation during testing can be effectively overcome using this sensor.Two different arrangements of the sensors are tested in verification tests.Additionally,the application of the sensor in an excavated-induced slope model is tested.The results demonstrate that the sensor exhibits commendable performance and achieves a desirable level of accuracy,with a principal stress angle error of±5°in the verification tests.The stress transformation of the slope model,generated by excavation,is demonstrated in the application test by monitoring the two miniature principal stress(MPS)sensors.The sensor has a significant potential for measuring primary stress in landslide model tests and other geotechnical model experiments.

Influence of material characteristics on the failure mode and process of landslide dam

Landslide dams,as frequent natural hazards,pose significant risks to human lives,property,and ecological environments.The grading characteristics and density of dam materials play a crucial role in determining the stability of landslide dams and the potential for dam breaches.To explore the failure mechanisms and evolutionary processes of landslide dams with varying soil properties,this study conducted a series of flume experiments,considering different grain compositions and material densities.The results demonstrated that grading characteristics significantly influence landslide dam stability,affecting failure patterns,breach processes,and final breach morphologies.Fine-graded materials exhibited a sequence of surface erosion,head-cut erosion,and subsequent surface erosion during the breach process,while well-graded materials typically experienced head-cut erosion followed by surface erosion.In coarse-graded dams,the high permeability of coarse particles allowed the dam to remain stable,as inflows matched outflows.The dam breach model experiments also showed that increasing material density effectively delayed the breach and reduced peak breach flow discharge.Furthermore,higher fine particle content led to a reduction in the residual dam height and the base slope of the final breach profile,although the relationship between peak breach discharge and the content of fine and coarse particles was nonlinear.To better understand breach morphology evolution under different soil characteristics and hydraulic conditions,three key points were identified—erosion point,control point,and scouring point.This study,by examining the evolution of failure patterns,breach processes,and breach flow discharges under various grading and density conditions,offers valuable insights into the mechanisms behind landslide dam failures.

Spatial distribution of landslides in response to the geomorphometric constraints in Darma Valley,Kumaun Himalaya

The Kumaun Himalaya is well-known as a geologically and tectonically complex region that amplifies mass wasting processes,particularly landslides.This study attempts to investigate the interplay between landslide distribution and the lithotectonic regime of Darma Valley,Kumaun Himalaya.A landslide inventory comprising 295 landslides in the area has been prepared and several morphotectonic proxies such as valley floor width to height ratio(Vf),stream length gradient index(SL),and hypsometric integral(HI)have been used to infer tectonic regime.Morphometric analysis,including basic,linear,aerial,and relief aspects,of 59 fourth-order sub-basins,has been carried out to estimate erosion potential in the study area.The result demonstrates that 46.77%of the landslides lie in very high,20.32%in high,21.29%in medium,and 11.61%in low erosion potential zones respectively.In order to determine the key parameters controlling erosion potential,two multivariate statistical methods namely Principal Component Analysis(PCA)and Agglomerative Hierarchical Clustering(AHC)were utilized.PCA reveals that the Higher Himalayan Zone(HHZ)has the highest erosion potential due to the presence of elongated sub-basins characterized by steep slopes and high relief.The clusters created through AHC exhibit positive PCA values,indicating a robust correlation between PCA and AHC.Furthermore,the landslide density map shows two major landslide hotspots.One of these hotspots lies in the vicinity of highly active Munsiyari Thrust(MT),while the other is in the Pandukeshwar formation within the MT's hanging wall,characterized by a high exhumation rate.High SL and low Vf values along these hotspots further corroborate that the occurrence of landslides in the study area is influenced by tectonic activity.This study,by identifying erosionprone areas and elucidating the implications of tectonic activity on landslide distribution,empowers policymakers and government agencies to develop strategies for hazard assessment and effective landslide risk mitigation,consequently safeguarding lives and communities.

Using fracture mechanics method to analyze the failure mechanism and equilibrium equation of interfacial loess-mudstone landslides

Loess-mudstone landslides are common in the Loess Plateau.Investigations into the mechanical theory of loess-mudstone landslides have become a challenging undertaking due to the distinctive interfacial properties of loess-mudstone and the unique water sensitivity characteristics of mudstone.Hence,it is imperative to develop innovative mechanical models and mathematical equations specifically tailored to loess-mudstone landslides.In this study,we analyze the fracture mechanism of the loess-mudstone sliding zone using plastic fracture mechanics and develop a unique fracture yield model.To calculate the energy release rate during the expansion of the loess-mudstone interface tip region,the shear fracture energy G is applied,which reflects both the yield failure criterion and the fracture failure criterion.To better understand the instability mechanism of loess-mudstone landslides,equilibrium equations based on G are established for tractive,compressive,and tensile loess-mudstone landslides.Based on the equilibrium equation,the critical length Lc of the sliding zone can be used for the safety evaluation of loess-mudstone landslides.In this way,this study proposes a new method for determining the failure mechanism and equilibrium equation of loessmudstone landslides,which resolves their starting mechanism,mechanical equilibrium equations,and safety evaluation indicators,thus justifying the scientific significance and practical value of this research.

Long-term monitoring of active large-scale landslides for non-structural risk mitigation-integrated sensors and web-based platform

Large-scale deep-seated landslides pose a significant threat to human life and infrastructure.Therefore,closely monitoring these landslides is crucial for assessing and mitigating their associated risks.In this paper,the authors introduce the So Lo Mon framework,a comprehensive monitoring system developed for three large-scale landslides in the Autonomous Province of Bolzano,Italy.A web-based platform integrates various monitoring data(GNSS,topographic data,in-place inclinometer),providing a user-friendly interface for visualizing and analyzing the collected data.This facilitates the identification of trends and patterns in landslide behaviour,enabling the triggering of warnings and the implementation of appropriate mitigation measures.The So Lo Mon platform has proven to be an invaluable tool for managing the risks associated with large-scale landslides through non-structural measures and driving countermeasure works design.It serves as a centralized data repository,offering visualization and analysis tools.This information empowers decisionmakers to make informed choices regarding risk mitigation,ultimately ensuring the safety of communities and infrastructures.