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Developing a robust numerical method to model earthquake-induced landslides has long been a persistent challenge in the field of computational geotechnical engineering. Recently, the meshless methods based on nonlocal theory have piqued the interest of researchers. However, the application of nonlocal theory in seismic analysis is currently limited. This paper proposed a numerical framework based on the updated Lagrangian nonlocal general particle dynamics (UL-NGPD) method to analyze earthquake-induced landslide problems. The UL-NGPD method benefiting from the update support domain can capture the whole process of slope run-out induced by earthquakes. To enhance the numerical stability of the proposed method, several optimizing strategies are proposed. In the current framework, the seismic waves are input through the proposed boundary treatments of nonlocal form. Besides, a nonlocal friction model with velocity-weakening is proposed to accurately simulate the movement process of soils on a rocky sliding bed. The proposed framework is validated by the simulations of several classic problems, including the collapse of sand and the shake table test. The performance of the proposed approach is further demonstrated through simulating the landslides induced by earthquakes. The numerical results consistent with recorded data indicate that the UL-NGPD method has an excellent capacity to deal with earthquake-induced landslide problems. • A novel nonlocal theory-based UL-NGPD framework is established to analyze earthquake-induced landslides. • The boundary treatments in the UL-NGPD are introduced. • A nonlocal friction model with velocity-weakening is proposed to accurately simulate the movement process of soils. • Several numerical examples demonstrate the UL-NGPD framework's capability in handling complex landslide problems. [ABSTRACT FROM AUTHOR]

Sensitive clays, when subjected to large strains, exhibit a unique strain-softening behavior, transforming into a remolded liquid with remarkably low shear strength. When a slope fails, this behavior leads to the remolded clay moving away from its original position, triggering subsequent failures and catastrophic outcomes. To accurately predict such scenarios, it is crucial to incorporate realistic strain-softening characteristics into the constitutive soil model. This paper presents a novel yet practical strain-softening law developed by the authors that effectively captures the post-peak behavior of sensitive clays down to their remolded strength. The softening law is implemented in an elastoplastic Mohr-Coulomb model and incorporated into Anura3D, an open-source software that uses the Material Point Method to simulate large deformations. The constitutive model is calibrated by simulating the stress-strain behavior through direct shear tests conducted at three sensitive clay landslide locations. The accuracy of the overall numerical framework is assessed by predicting the post-failure movements of these landslides. Notably, two of the landslides, Sainte-Monique (1994) and Saint-Jude (2010), have previously been analyzed using other numerical tools, allowing for a comparative analysis with the method presented here. The third landslide, the Saint-Luc-de-Vincennes landslide, representing a composite flow slide and spread, has been numerically simulated for the first time. The post-failure behavior observed in the landslide events is compared with field observations and other numerical analyses. The results show that the MPM models with the proposed strain-softening law can reasonably predict post-failure retrogression and runout distance, which are crucial parameters for determining the risk of landslides in sensitive clays. • A new strain-softening law for sensitive clays in the MPM framework accurately simulates stress-strain behavior. • The new softening law allows for the prediction of large strain parameters without rigorous back analysis. • The numerical framework successfully captures post-failure behavior of prehistoric retrogressive failures. • Comparing simulation results with field observations shows that the softening law produces reasonable outcomes. • The Saint-Luc-de-Vincennes landslide is numerically modeled for the first time. [ABSTRACT FROM AUTHOR]

Earthquake-induced landslides are associated with significant risks to human lives and infrastructure. Spatially distributed infrastructure systems, such as pipelines, power lines, and transportation networks, are at particular risk to seismic landslides due to their large spatial extent. To conduct a comprehensive seismic landslide risk assessment for these systems, there is the need to evaluate the seismic landslide characteristics (i.e., location, size, displacement, direction) on a broad, regional scale. This paper presents a framework for seismic landslide analysis that provides this information for subsequent risk assessments. The approach computes seismic landslide displacements using a sliding block approach while accounting for the uncertainties in the input variables and displacement models using a logic tree. The computed displacements are then aggregated based on geomorphic landforms to define landslide zones. For each landslide zone, the statistical distributions of landslide features, such as landslide size, displacement level, and direction of movement, are defined. These attributes are presented in a format that can be integrated with fragility models for distributed infrastructure systems to quantify risk on a regional scale. The application of the approach is demonstrated through assessments for gas pipeline networks across the state of California in the United States. • A framework for regional seismic landslide risk assessment is described. • A sliding block approach with a logic tree accounts for uncertainties. • Geomorphic landforms (slope units) are integrated to define landslide zones. • Distributions of landslide attributes are defined for input into fragility models. • Results are shown for a region of southern California. [ABSTRACT FROM AUTHOR]

Advances in numerical methods have provided useful tools for investigating the complex behaviour of landslides, which can be a valuable support for landslide hazard assessment, planning, and design of mitigation measures. Among different methodologies, the Material Point Method (MPM) has recently grown in popularity, thanks to its ability to simulate large displacements and has been applied to simulate an increasing number of real cases. Despite the success, there are still several challenges to be addressed. This paper aims to present the current state of the art in the modelling of real landslide case histories with MPM. The key numerical features used to capture the evolution of different types of landslides are discussed, such as constitutive models, soil-water interaction, and triggering mechanisms, thus providing insight into the computational aspects of using MPM to serve as guidelines for future applications. Limitations and future perspectives are also mentioned to encourage the development of new solutions for current numerical challenges and further extend the applicability of the methodology in this field. • Material Point Method (MPM) has been used to simulate a large number of real landslides. A comprehensive review is presented. • MPM can incorporate many key features crucial for advancing landslide understanding and risk management. • Constitutive models, soil-water interaction, and triggers are discussed to provide guidelines for future applications. • Despite the advances of the last decades, additional work is still needed to improve the methodology. [ABSTRACT FROM AUTHOR]

Türkiye is known for its intricate tectonic structure and high seismic activity, which makes in-depth research on earthquakes necessary. This research paper presents a multifaceted examination of earthquake impact in Türkiye, encompassing critical elements of seismic vulnerability with a special focus on the latest twin earthquake of February 6, 2023. Firstly, a three-dimensional finite element (FE) analysis has been conducted to explore the buried pipeline behavior under left-lateral strike-slip faults. Secondly, making use of the earthquake data from 1976 to 2023, a new Newmark displacement-based model has been proposed which predicts co-seismic landslide displacement. Finally, new magnitude conversion relations are developed in the generalized moment magnitude scale (M wg) for Türkiye using three statistical regression techniques. The maximum tensile strain was found to be around 2.0% based on the findings of the 3D numerical study of an underground pipe that was subjected to left-lateral strike-slip fault movement. The proposed co-seismic landslide displacement model was developed based on moment magnitude, yield acceleration, and peak ground acceleration, which yields an R-squared value of 0.93. Outcomes of the magnitude conversion study depicted that the improved general orthogonal regression (IGOR) relationships developed in this study performed better than the other conventional methods of regression. Also, the proposed regional relations based on M wg scale perform better than global relations as they provide closer values to the observed ones. By offering a comprehensive perspective on seismic impact, the research lays the groundwork for upgraded predictive methods and refined seismic design considerations. The proposed models and conversion relations contribute to the strengthening of critical infrastructures, providing a forward-looking approach to enhance preparedness and resilience against earthquake disasters in Türkiye. • Examined the response of buried pipelines under left-lateral strike-slip fault. • Proposed a new empirical model for co-seismic landslide displacement for Türkiye. • Developed magnitude conversion relations in the M wg scale for Türkiye. [ABSTRACT FROM AUTHOR]

Co-seismic landslides can cause devastating consequences in seismically active mountainous regions. Rapid assessment of co-seismic landslides is essential in effective emergency response. A dilemma that hinders the assessment is the presence of a significant bias, either overestimating or underestimating the actual landslide hazard. To overcome this dilemma, this paper proposes a two-phase strategy that involves constructing a hierarchical Bayesian model augmenting data from a new earthquake with historical co-seismic landslide inventories. Our findings demonstrate that even with limited and evenly distributed co-seismic data from the new earthquake, it is possible to achieve an unbiased and accurate assessment. The two-phase strategy can facilitate the rapid and unbiased assessment of co-seismic landslides, thereby informing policy-making and resource allocation. • Co-seismic landslide assessment is often biased, either over- or underestimating landslide hazard. • A two-phase strategy is proposed based on the availability of co-seismic landslide data. • A hierarchical Bayesian model is implemented in the two-phase strategy. • The two-phase strategy enables unbiased and accurate co-seismic landslide assessment. • The rapid and unbiased assessment informs emergency response and resource allocation. [ABSTRACT FROM AUTHOR]

Plowing refers to the phenomenon by which a landslide displaces and pushes the path material forward. Despite its importance in shaping the dynamics of many rapid flow-like loess landslides, the mechanism of plowing is not fully understood. Therefore, this paper presents an improved numerical model based on the material point method (MPM). This model takes into account the interactions between the landslide mass and the path material. The reliability of this model was validated by simulating two granular column collapse experiments involving interaction with an erodible mass. The model was then applied to analyze the plowing dynamics of the Ximiaodian loess landslide in Jingyang County, Shaanxi Province, China. A series of well-designed simulations were conducted to investigate this specific landslide event, contributing to a deeper understanding of the plowing phenomenon in loess landslides. The simulation results show that our model can satisfactorily simulate the plowing process of the Ximiaodian landslide. The plowing process of a rapid-flow loess landslide is proposed to consist of four distinct stages: 1) the collision stage, when the landslide strikes the terrace, resulting in the bending of its strata; 2) the shear failure stage, characterized by significant plastic shear deformation within the bent strata, resulting in the formation of multiple shear failure planes; 3) the steady plowing stage, during which the deformation structure in the affected terrace stabilizes and plowing continues in a steady pattern; and 4) the stopping stage, when the propagation of the disturbed terrace gradually stops. Furthermore, the disturbed terrace zone exhibits three distinct deformation structures. In the rear part, a stable zone develops where the terrace layers are compressed. However, due to the compressive forces exerted by the overlying loess, no significant shear deformation is observed. The middle part is the shear failure zone, characterized by the formation of multiple shear failure planes. The front part is the upheaval zone, where the terrace layers are slightly bent and tilted. • An improved MPM model is proposed to simulate the plowing process of rapid flow-like landslides. • The plowing mechanism of a real loess landslide is analyzed by the MPM model for the first time. • The plowing process of a rapid flow-like loess landslide consists of four stages. • Three distinct deformation structures are formed in the disturbed terrace zone during the plowing process. [ABSTRACT FROM AUTHOR]

Effective detection of groundwater in landslide areas is crucial for landslide monitoring. The magnetic resonance sounding (MRS) method is a geophysical technique that can directly detect groundwater. However, its application in landslide groundwater monitoring is often hindered by the complex topography of landslides. This paper presents an MRS framework that accounts for the impact of landslide topography on groundwater detection. The framework includes the use of drone photogrammetry to obtain high-precision topography data and real-time kinematic equipment to acquire the coordinates of key points along the MRS coil. A three-dimensional finite element model is then constructed to calculate the alternating magnetic field generated by the MRS coil and the kernel function required for MRS inversion. Two typical landslide topography models—terrace and gully—are constructed and analyzed using numerical simulations. The results indicate that landslide topography significantly impacts the quality of groundwater information retrieved by MRS in both one-dimensional and two-dimensional inversions, affecting the accurate detection and identification of infiltration surfaces, sliding zone water, and bedrock surfaces. If accurate topography data are obtained and appropriate kernel functions are calculated, the accuracy of the inversion can be greatly improved. An example of using the MRS method to detect groundwater in a complex landslide in the Three Gorges Reservoir area of China is then presented. The MRS inversion results that consider landslide topography are confirmed using borehole data, electrical resistivity tomography detection results, and on-site drainage tunnel investigations. The water-content distribution inside the landslide body is obtained, providing information helpful to define sliding zones in multi-level landslides. The proposed landslide MRS detection framework makes it possible to apply MRS methods to the detection of landslide groundwater distribution, analysis of seepage fields, and discrimination of landslide geological structures. • Landslide topography significantly affects the quality of groundwater information retrieved by MRS. • Obtaining accurate topography data and calculating appropriate kernel functions improve MRS inversion results. • MRS field work on a complex landslide is validated by borehole data, ERT, and on-site drainage tunnel investigations. [ABSTRACT FROM AUTHOR]

Reservoir operations can induce massive landslides due to the regional hydraulic effects, where pile reinforcement is a widely used stabilization method. The 1-g physical modeling is one of the most common methods used to study reservoir landslides and their pile reinforcement, while some limitations have been presented in the method, slowed its development down, and adverse to novel results. This paper presents a systematic review and discussion of 1-g physical modeling relevant to pile-reinforced reservoir landslides. Various aspects, from the theoretical basis (i.e., principle of similitude) to experimental details (i.e., model construction, test technique, and data acquisition and interpretation), are investigated. The principle of similitude and application in the physical modeling of pile-reinforced reservoir landslides are discussed. Factors causing distortions in a 1-g physical landslide model are discussed, and measures for distortion control are proposed. The significance and limitations of the physical model construction and obtained data are analyzed, and some improvements are proposed. Based on previous work and our experience, the lessons learned in the technical aspects of 1-g physical modeling are presented along with recommendations for the physical modeling of landslides, especially those reinforced with piles. • A strategy of distortion compensation in 1-g physical modeling is provided as a supplement to the principle of similitude. • The lessons in technical aspects of 1-g physical modeling of reservoir landslides and pile reinforcement are discussed. • The displacement and pressure data limitations are analyzed and its improvements are proposed. [ABSTRACT FROM AUTHOR]

This paper proposes a new framework for discretizing the conditional random fields that combines the Hoffman method and Bayesian updating, where multiple types of geotechnical data can be utilized, such as the field monitoring data and the site investigation geological borehole data. The feasibility of the proposed method is demonstrated using a rainfall-induced slope example in spatially varying soils. Results showed that the proposed framework of incorporating Bayesian updating to Hoffman method can effectively reduce the uncertainty in site characterization and guarantee precise risk assessment of unsaturated slope stability. Otherwise, the slope failure probability and landslide sliding volume would be overestimated when only the Hoffman method is adopted for site characterization. Subsequently, the developed method is further used to estimate the probabilistic stability of Baishuihe landslide in the presence of rainfall infiltration and reservoir water level fluctuation. It is hoped that this study can provide a reference for risk assessment of the unsaturated slope stability in reservoir areas. • A novel conditional random field generation method is developed. • Site investigation and field monitoring data can be used for site characterization. • Effectiveness of the proposed method is verified via a rainfall-induced slope. • Baishuihe landslide is investigated under rainfall and reservoir water fluctuation. [ABSTRACT FROM AUTHOR]