Identification of failure slip surfaces for landslide risk assessment using smoothed particle hydrodynamics

Li, Liang; Wang, Yu

doi:10.1080/17499518.2019.1602877

Cited by 24 publications

(13 citation statements)

References 39 publications

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“…As has been reported in Li et al (2019), the slip surface with FS min in LEM or SRM exhibits a significant difference from the true slip surface during landslide, and the true slip surface should be properly identified to calculate the area of sliding mass and to calculate the consequence C . In this paper, the true slip surface is determined using an in-house FORTRAN-based SPH software package which was developed by the authors and validated against laboratory experimental results and simulation of landslides (Li et al , 2019; Li and Wang, 2019).…”

Section: Sph-based Response Surface Methodsmentioning

confidence: 99%

“…SPH has been widely used in astrophysics (Lucy, 1977; Gingold and Monaghan, 1977), hydrodynamics and aeronautics (Liu and Liu, 2004), and it has been extended to geotechnical engineering (Bui et al , 2011; An et al , 2016; Mao et al , 2017; Zhang and Zhou, 2018; Bao et al , 2018; Li and Wang, 2019; Zhang et al , 2019; Li et al , 2019), as elasto-plastic constitutive models for soils have recently been implemented into SPH formulation (Bui et al , 2008). To date, SPH serves as an alternative and effective method to simulate the full process of a slope failure including the initiation, propagation and deposition of sliding mass.…”

Section: Review Of Sph Fundamentalsmentioning

confidence: 99%

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Cohesive slope failure analysis using methods combining smoothed particle hydrodynamics and response surface function

Chu

Guo

2019

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Purpose The paper aims to construct a method to simulate the relationship between the parameters of soil properties and the area of sliding mass of the true slip surface of a landslide. Design/methodology/approach The smoothed particle hydrodynamics (SPH) algorithm is used to calibrate a response surface function which is adopted to quantify the area of sliding mass of the true slip surface for each failure sample in Monte Carlo simulation. The proposed method is illustrated through a homogeneous and a heterogeneous cohesive soil slope. Findings The comparison of the results between the proposed method and the traditional method using the slip surface with minimum factor of safety (FSmin) to quantify the failure consequence has shown that the landslide risk tends to be attributed to a variety of risk sources, and that the use of a slip surface with FSmin to quantify the consequence of a landslide underestimates the landslide risk value. The difference of the risk value between the proposed method and the traditional method increases dramatically as the uncertainty of soil properties becomes significant. Practical implications A geotechnical engineer could use the proposed method to perform slope failure analysis. Originality/value The failure consequence of a landslide can be rationally predicted using the proposed method.

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Section: Sph-based Response Surface Methodsmentioning

confidence: 99%

Section: Review Of Sph Fundamentalsmentioning

confidence: 99%

Cohesive slope failure analysis using methods combining smoothed particle hydrodynamics and response surface function

Chu

Guo

2019

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“…SPH has also been widely used in many research fields such as fluid mechanics [28][29][30] and geotechnical engineering [31][32][33][34][35][36][37][38][39][40][41][42] since the constitutive model was first implemented in SPH by Bui et al [43].…”

Section: Brief Review Of Smoothed Particle Hydrodynamics (Sph)mentioning

confidence: 99%

“…e better results from SPH have inspired the authors to further investigate the application of meshless methods in slope stability analysis. An in-house Fortran-based SPH software package has been developed and has been validated through comparison with SPH and FEM results as well as physical model test results reported in the literature [38]. is software package is used to model the slope failure process.…”

Section: Brief Review Of Smoothed Particle Hydrodynamics (Sph)mentioning

confidence: 99%

On the Location of Multiple Failure Slip Surfaces in Slope Stability Problems Using the Meshless SPH Algorithm

Zhai

Ling

et al. 2020

Advances in Civil Engineering

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The occurrence of multiple critical slip surfaces with equal importance in rehabilitating and reinforcing slopes has been frequently observed in geotechnical engineering practices. The simultaneous determination of these potential slip surfaces is, however, not trivial. This paper presents a methodology based on the smoothed particle hydrodynamics (SPH) approach, which can simultaneously determine multiple failure slip surfaces and the debris flow process without previous knowledge or trial-and-error processes, and this methodology is validated against a slope with the presence of multiple critical slip surfaces. The proposed methodology serves as an efficient and effective alternative approach to traditional approaches, which involve cumbersome treatments performed by engineers based on their subjective experiences. The multiple sources of failure slip surfaces in slope stability are equivalent to multiple sources of initiation of slope failure, and it is found that SPH can provide a direct and systematic tool for identifying multiple failure slip surfaces. However, some minor potential problems are also found with the use of the SPH method in actual applications.

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“…Many studies on the landslide risk assessment have already been carried out using a variety of methods [40][41][42][43][44]. They are qualitative, semi-quantitative, and quantitative meth-ods [2].…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment of Resources Exposed to Rainfall Induced Landslide with the Development of GIS and RS Based Ensemble Metaheuristic Machine Learning Algorithms

et al. 2021

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Disastrous natural hazards, such as landslides, floods, and forest fires cause a serious threat to natural resources, assets and human lives. Consequently, landslide risk assessment has become requisite for managing the resources in future. This study was designed to develop four ensemble metaheuristic machine learning algorithms, such as grey wolf optimized based artificial neural network (GW-ANN), grey wolf optimized based random forest (GW-RF), particle swarm optimization optimized based ANN (PSO-ANN), and PSO optimized based RF for modeling rainfall-induced landslide susceptibility (LS) in Aqabat Al-Sulbat, Asir region, Saudi Arabia, which observes landslide frequently. To obtain very high precision and robust prediction from machine learning algorithms, the grey wolf and PSO optimization algorithms were integrated to develop new ensemble machine learning techniques. Subsequently, LS maps produced by training dataset were validated using the receiver operating characteristics (ROC) curve based on the testing dataset. Based on the area under curve (AUC) value of ROC curve, the best method for LS modeling was selected. We developed ROC curve-based sensitivity analysis to investigate the influence of the parameters for LS modeling. The Gumble extreme value distribution was employed to estimate the rainfall at 2, 5, 10, 20, 50, and 100 year return periods. Then, the landslide hazard maps were prepared at different return periods by integrating the best LS model and estimated rainfall at different return periods. The theory of danger pixels was employed to prepare a final risk assessment of the resources, which have been exposed to the landslide. The results showed that 27–42 and 6–15 km2 were predicted as the very high and high LS zones using four ensemble metaheuristic machine learning algorithms. Based on the area under curve (AUC) of ROC, GR-ANN (AUC-0.905) appeared as the best model for LS modeling. The areas under high and very high landslide hazard were gradually increased over the progression of time (26 km2 at the 2 year return period and 40 km2 at the 100 year return period for the high landslide hazard zone, and 6 km2 at the 2 year return period and 20 km2 at the 100 year return period for the very high landslide hazard zone). Similarly, the areas of danger pixel also increased gradually from the 2 to 100 year return periods (37 km2 to 62 km2). Various natural resources, such as scrubland, built up, and sparse vegetation, were identified under risk zone due to landslide hazards. In addition, these resources would be exposed extensively to landslides over the advancement of return periods. Therefore, the outcome of the present study will help planners and scientists to propose high precision management plans for protecting natural resources, which have been exposed to landslides.

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Identification of failure slip surfaces for landslide risk assessment using smoothed particle hydrodynamics

Cited by 24 publications

References 39 publications

Cohesive slope failure analysis using methods combining smoothed particle hydrodynamics and response surface function

Cohesive slope failure analysis using methods combining smoothed particle hydrodynamics and response surface function

On the Location of Multiple Failure Slip Surfaces in Slope Stability Problems Using the Meshless SPH Algorithm

Risk Assessment of Resources Exposed to Rainfall Induced Landslide with the Development of GIS and RS Based Ensemble Metaheuristic Machine Learning Algorithms

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