Stability mechanism recognition and failure risk assessment on a high slope by synthesizing different analysis methods

Qi, Changqing; Li, Liuyang; Li, Ruoqi; Gan, Feifei; Zhang, Wentao; Han, Hui

doi:10.1007/s11069-021-04745-2

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…So far, studies on the mechanical properties of the S-RM are in progress, employing mechanical tests predominantly, and the large-scale apparatuses are more preferred to avoid the constraints on the motions of rock fragments inside because of the equipment boundary. The numerical simulation methods on the S-RM could be divided into continuous and discontinuous medium simulations (Ding et al, 2010;Fu et al, 2017;Fu et al, 2019;Liu et al, 2020;Liu et al, 2021;Meng et al, 2018;Qi et al, 2021;Yang et al, 2020;Zou et al, 2011). The test and simulation results show that the mechanical properties are affected by several influencing factors such as rock block proportion, water content, compactness, and granulometric composition, and the rock block proportion plays an important role in the mechanical properties (Afifipour and Moarefvand, 2014;Monkul and Ozden, 2007;Xu et al, 2011;Yang Y. et al, 2016;Chang and Phantachang, 2016;Xu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Applications of an Innovative Strength Parameter Estimation Method of the SoilRock Mixture in Evaluating the Deposit Slope Stability Under Rainfall

Zhu

Sheng

et al. 2021

Front. Earth Sci.

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Deposit landslides are one of the most representative geological hazards around the world, and the poor understanding of the mechanical characteristics of the soilrock mixture (S-RM) increases the difficulties in evaluating the stability of the deposit slope subjected to rainfall. This study attempted to propose an innovative strength parameter estimation method of the S-RM based on the concept and format of the generalized HoekBrown criterion. The system of “rock blocks–soil–contact surface” was considered the main source providing resistance to the external force, and the effect of the soil matrix, rock blocks, and the contact surface between them was taken into account. The verification of the estimation method was proved based on the abundant test data. A typical deposit slope located in the Taoyuan region of Yunnan Province, China, was selected as a case study, and the estimation method was applied to provide the shear parameters of the S-RM. The slope stability subjected to different types of rainfall was analyzed considering the parameter deteriorations, and the results show that the deposit landslide is more prone to the wave-type rainfall, which shows a deeper response depth of pore water pressure and a smaller safety factor. The present study suggests a simple estimation method for the shear parameters of the S-RM using a few parameters for the engineers and provides an understanding of the deposit slope stability during the rainfall process.

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Section: Introductionmentioning

confidence: 99%

Applications of an Innovative Strength Parameter Estimation Method of the SoilRock Mixture in Evaluating the Deposit Slope Stability Under Rainfall

Zhu

Sheng

et al. 2021

Front. Earth Sci.

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Topological Mapping and Risk Assessment of High Slope Engineering Deformation Time Series from the Perspective of Complex Network

Han,

Shen

2024

Lecture Notes in Operations Research

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Dam failure risk analysis of earthen and rockfill dam systems: an approach based on a combination of an Interpreted Structural Model and a Bayesian Network with parameter learning

Li,

Zhang,

Wang

et al. 2023

Preprint

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The risk factors of earthen and rockfill dams during operation are characterized by uncertainty, complexity, interaction, etc. The coupling of risk factors can be more accurately identified in the process of dam risk analysis. To effectively analyze the interactions between the influencing factors within the system, this paper proposes a method for analyzing the risk of earthen and rockfill dam failure based on a combination of the Interpretive Structural Model (ISM) and Bayesian network (BN) model with the parameter learning. Meanwhile, the parameter learning of the BN model using the EM algorithm reduces the subjectivity of expert evaluation. In this paper, we analyzed the interrelationships among accidents by using the ISM method through statistics and analysis of actual accident cases. We established a hierarchical structure diagram including a five-level structure to derive the direct, indirect, and fundamental factors that lead to accidents. The EM algorithm was introduced to learn Bayesian network parameters, and the probability of occurrence of each influencing factor of earthen and rockfill dam failure was obtained through BN inference, diagnosis, and sensitivity analysis. The three most important influencing factors leading to earthen and rockfill dam failure were identified as flood overtopping, insufficient spillway discharge capacity, and damage to the spillway structure. A multi-factor coupling analysis was also conducted on the earthen and rockfill dams, and the results showed that the risk of dam failure was greatly increased as a result of the coupling between the influencing factors. In addition, we also found that management issues play an important role in earthen and rockfill dam failures and are key influencing factors that cannot be ignored. This method can be effectively applied to identify and analyze the influencing factors of earthen and rockfill dam failure in China.

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Stability mechanism recognition and failure risk assessment on a high slope by synthesizing different analysis methods

Cited by 3 publications

References 17 publications

Applications of an Innovative Strength Parameter Estimation Method of the SoilRock Mixture in Evaluating the Deposit Slope Stability Under Rainfall

Applications of an Innovative Strength Parameter Estimation Method of the SoilRock Mixture in Evaluating the Deposit Slope Stability Under Rainfall

Topological Mapping and Risk Assessment of High Slope Engineering Deformation Time Series from the Perspective of Complex Network

Dam failure risk analysis of earthen and rockfill dam systems: an approach based on a combination of an Interpreted Structural Model and a Bayesian Network with parameter learning

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