Combined methodology for three-dimensional slope stability analysis coupled with time effect: a case study in Germany

Dong, Mei; Hu, Hui; Song, Jie

doi:10.1007/s12665-018-7497-0

Cited by 10 publications

(6 citation statements)

References 21 publications

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“…4 to derive the following equation: Furthermore, slope stability 3D calculation methods are increasingly being used for calculation applications, although not as much as 2D analysis. Research and the latest state of the art regarding slope stability 3D have been conducted by several researchers [23]- [28]. This research attempts to see how the effectiveness of slope stability 3D analysis compared to 2D analysis.…”

Section: B 3-dimensional Slope Stability Analysismentioning

confidence: 99%

The Comparison Between 2-D and 3-D Slope Stability Analysis Based on Reinforcement Requirements

Sari¹,

Putri²,

Savitri³

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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The landslides in the field often do not have an infinite length, making 3-dimensional assumptions more appropriate for the design. Meanwhile, they are mostly analyzed in design by assuming the landslides occur infinitely with plane strain in a 2dimensional approach. This assumption becomes less precise due to the consideration of the safety factor based on 2-dimensional conditions while the landslide happens 3-dimensional, and this has further effects on the need for reinforcement. This research was conducted to determine the level of influence 2-dimensional and 3-dimensional landslide safety factors have on the number of reinforcements required. A limit equilibrium method was used in the study. The number of geotextile reinforcement needs with a 3dimensional sliding model is calculated based on existing research results. The results are then compared with the amount of geotextile reinforcement required with a 2-dimensional sliding model. The results showed the possibility of having the same amount of reinforcement under 2-D and 3-D assumptions in the homogeneous soil, while different results were found with heterogeneous soil layers due to the variations in soil conditions. Comparison of the number of geotextile reinforcement requirements between 2 dimensions and three dimensions still needs to be further developed by using more varied soil data. This is done considering that existing studies are only limited to soil, which tends to be homogeneous.

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Section: B 3-dimensional Slope Stability Analysismentioning

confidence: 99%

The Comparison Between 2-D and 3-D Slope Stability Analysis Based on Reinforcement Requirements

Sari¹,

Putri²,

Savitri³

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Slope deformation is an external manifestation of a combination of multiple factors, such as land type, geological condition, rainfall, fluctuation in groundwater level, extent of vegetation and seismic loads. At present, research on the prediction and early warning of landslide disasters is mainly divided into regional landslide susceptibility prediction [27,28] and single landslide deformation prediction [29][30][31]. Regional landslide susceptibility prediction focuses on the spatial probability of landslide occurrence in a given region [32].…”

Section: Introductionmentioning

confidence: 99%

“…Cai et al (2015) [47] optimized the least squares support vector machine for landslide displacement prediction by using a genetic algorithm that considered multiple influencing factors. Dong et al (2018) [31] used a time series analysis combined with a 3D geological model and a finite-element simulation method to improve the reliability of the calculated factor of safety. The acquisition of environmental factors, spatial analysis and data management of deformation prediction of unstable slopes were mainly based on monitoring technology.…”

Section: Introductionmentioning

confidence: 99%

Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR Model

Dong

Hu³

et al. 2020

Sensors

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With increased urbanization, accidents related to slope instability are frequently encountered in construction sites. The deformation and failure mechanism of a landslide is a complex dynamic process, which seriously threatens people’s lives and property. Currently, prediction and early warning of a landslide can be effectively performed by using Internet of Things (IoT) technology to monitor the landslide deformation in real time and an artificial intelligence algorithm to predict the deformation trend. However, if a slope failure occurs during the construction period, the builders and decision-makers find it challenging to effectively apply IoT technology to monitor the emergency and assist in proposing treatment measures. Moreover, for projects during operation (e.g., a motorway in a mountainous area), no recognized artificial intelligence algorithm exists that can forecast the deformation of steep slopes using the huge data obtained from monitoring devices. In this context, this paper introduces a real-time wireless monitoring system with multiple sensors for retrieving high-frequency overall data that can describe the deformation feature of steep slopes. The system was installed in the Qili connecting line of a motorway in Zhejiang Province, China, to provide a technical support for the design and implementation of safety solutions for the steep slopes. Most of the devices were retained to monitor the slopes even after construction. The machine learning Probabilistic Forecasting with Autoregressive Recurrent Networks (DeepAR) model based on time series and probabilistic forecasting was introduced into the project to predict the slope displacement. The predictive accuracy of the DeepAR model was verified by the mean absolute error, the root mean square error and the goodness of fit. This study demonstrates that the presented monitoring system and the introduced predictive model had good safety control ability during construction and good prediction accuracy during operation. The proposed approach will be helpful to assess the safety of excavated slopes before constructing new infrastructures.

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“…In recent years, with the need for social development, the mining of mineral resources has continued, and the stability of the slope has been greatly influenced as the excavation depth has deepened [1]- [3]. In particular, the mining process of large open pit mines can form many steep slopes, which are formed by blasting or destruction of large equipment.…”

Section: Introductionmentioning

confidence: 99%

Research on a Method of Gross Error Elimination for Slope Monitoring Data Based on Machine Learning

Xiao

et al. 2019

IEEE Access

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Many high and steep slopes are formed in the open pit mining process, whose stability will directly affect the construction and safety. Therefore, it is particularly important to detect and eliminate rough errors of slope monitoring data. This paper proposes an improved two-layer extreme learning machine (TELM) based on particle swarm optimization (PSO) to establish monitoring slope model. In particular, the parameters of the first layer of the whole algorithm are obtained by particle swarm optimization algorithm, and the parameters of the second layer are calculated through a new solution method. First, some basic principles and concepts of extreme learning machine are introduced, and the algorithm thought, basic principles and related derivation of the improved two-layer extreme learning machine in this paper are emphatically discussed. A better mapping relationship between the input space and the output space is found through the parameter solution method, which improves the precision of the algorithm. Then, the data collection and traditional gross error elimination methods are studied. Finally, in order to test the ability of the two hidden layer extreme learning machine to solve the slope monitoring problem, the algorithm is applied to the gross error elimination of the slope data. The experimental results show that compared with the traditional gross error elimination method and ELM algorithm, the proposed method has higher precision, stronger robustness, and more practicability. INDEX TERMS Extreme learning machine (ELM), gross error elimination, two-layer extreme learning machine, particle swarm optimization.

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Combined methodology for three-dimensional slope stability analysis coupled with time effect: a case study in Germany

Cited by 10 publications

References 21 publications

The Comparison Between 2-D and 3-D Slope Stability Analysis Based on Reinforcement Requirements

The Comparison Between 2-D and 3-D Slope Stability Analysis Based on Reinforcement Requirements

Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR Model

Research on a Method of Gross Error Elimination for Slope Monitoring Data Based on Machine Learning

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