A physical model predicting instability of rock slopes with locked segments along a potential slip surface

Chen, Hongran; Qin, Siqing; Xue, Lei; Yang, Baicun; Zhang, Ke

doi:10.1016/j.enggeo.2018.05.012

“…The modeling processes of data-based models are simpler and more accurate than those of physically-based models [12]. Nevertheless, accurate prediction of the deformation behavior of slopes remains a challenge [13,14]. It is well known that slope failure is the result of the action of nonlinear dynamical systems [15], and its deformation and stability are influenced by multifactorial factors, including geotechnical properties, hydrogeology, geomorphological conditions, climate, weathering, vegetation, and human engineering activities, the interaction, of which renders slope failure randomness, fuzziness, and variability [16,17].…”

Section: Displacement Prediction Of Natural and Human-induced Slopesmentioning

confidence: 99%

A Deep Learning Approach Using Graph Convolutional Networks for Slope Deformation Prediction Based on Time-series Displacement Data

Ma¹,

Mei²,

Zhang³

et al. 2020

Preprint

2

0

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Slope deformation prediction is crucial for early warning of slope failure, which can prevent property damage and save human life. Existing predictive models focus on predicting the displacement of single monitoring points based on time series data, without considering spatial correlations among monitoring points, which makes it difficult to reveal the displacement changes in the entire monitoring system, and ignores the potential threats from nonselected points. To address the above problem, this paper presents a novel deep learning method for predicting the slope deformation, by considering the spatial correlations between all points in the entire displacement monitoring system. The essential idea behind the proposed method is to predict the slope deformation based on the global information (i.e., the correlated displacements of all points in the entire monitoring system), rather than based on the local information (i.e., the displacements of a specified single point in the monitoring system). In the proposed method, (1) a weighted adjacency matrix is built to interpret the spatial correlations between all points, (2) a feature matrix is assembled to store the time-series displacements of all points, and (3) one of the state-of-the-art deep learning models, i.e., T-GCN, is developed to process the above graph-structured data consisting of two matrices. The effectiveness of the proposed method is verified by performing predictions based on a real dataset. The proposed method can be applied to predict time-dependency information in other similar geohazard scenarios, based on time-series data collected from multiple monitoring points.

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“…The modeling processes of data-based models are simpler and more accurate than those of physically-based models [28]. Nevertheless, accurate prediction of the deformation behavior of slopes remains a challenge [9,11]. It is well known that slope failure is the result of the action of nonlinear dynamical systems [39], and its deformation and stability are influenced by multifactorial factors, including geotechnical properties, hydrogeology, geomorphological conditions, climate, weathering, vegetation, and human engineering activities, the interaction, of which renders slope failure randomness, fuzziness, and variability [15,43].…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach using graph convolutional networks for slope deformation prediction based on time-series displacement data

Ma

¹

,

Mei

²

,

Prezioso

³

et al. 2021

Neural Comput & Applic

View full text Add to dashboard Cite

Slope deformation prediction is crucial for early warning of slope failure, which can prevent property damage and save human life. Existing predictive models focus on predicting the displacement of a single monitoring point based on time series data, without considering spatial correlations among monitoring points, which makes it difficult to reveal the displacement changes in the entire monitoring system and ignores the potential threats from nonselected points. To address the above problem, this paper presents a novel deep learning method for predicting the slope deformation, by considering the spatial correlations between all points in the entire displacement monitoring system. The essential idea behind the proposed method is to predict the slope deformation based on the global information (i.e., the correlated displacements of all points in the entire monitoring system), rather than based on the local information (i.e., the displacements of a specified single point in the monitoring system). In the proposed method, (1) a weighted adjacency matrix is built to interpret the spatial correlations between all points, (2) a feature matrix is assembled to store the time-series displacements of all points, and (3) one of the state-of-the-art deep learning models, i.e., T-GCN, is developed to process the above graph-structured data consisting of two matrices. The effectiveness of the proposed method is verified by performing predictions based on a real dataset. The proposed method can be applied to predict time-dependency information in other similar geohazard scenarios, based on time-series data collected from multiple monitoring points.

show abstract

“…The modeling processes of data-based models are simpler and more accurate than those of physically-based models [28]. Nevertheless, accurate prediction of the deformation behavior of slopes remains a challenge [9,11]. It is well known that slope failure is the result of the action of nonlinear dynamical systems [39], and its deformation and stability are influenced by multifactorial factors, including geotechnical properties, hydrogeology, geomorphological conditions, climate, weathering, vegetation, and human engineering activities, the interaction, of which renders slope failure randomness, fuzziness, and variability [43,15].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach Using Graph Convolutional Networks for Slope Deformation Prediction Based on Time-series Displacement Data

Ma¹,

Mei²,

Prezioso³

et al. 2021

Preprint

View full text Add to dashboard Cite

Slope deformation prediction is crucial for early warning of slope failure, which can prevent property damage and save human life. Existing predictive models focus on predicting the displacement of single monitoring points based on time series data, without considering spatial correlations among monitoring points, which makes it difficult to reveal the displacement changes in the entire monitoring system, and ignores the potential threats from nonselected points. To address the above problem, this paper presents a novel deep learning method for predicting the slope deformation, by considering the spatial correlations between all points in the entire displacement monitoring system. The essential idea behind the proposed method is to predict the slope deformation based on the global information (i.e., the correlated displacements of all points in the entire monitoring system), rather than based on the local information (i.e., the displacements of a specified single point in the monitoring system). In the proposed method, (1) a weighted adjacency matrix is built to interpret the spatial correlations between all points, (2) a feature matrix is assembled to store the time-series displacements of all points, and (3) one of the state-of-the-art deep learning models, i.e., T-GCN, is developed to process the above graph-structured data consisting of two matrices. The effectiveness of the proposed method is verified by performing predictions based on a real dataset. The proposed method can be applied to predict time-dependency information in other similar geohazard scenarios, based on time-series data collected from multiple monitoring points.

show abstract

A physical model predicting instability of rock slopes with locked segments along a potential slip surface

Cited by 74 publications

References 43 publications

A Deep Learning Approach Using Graph Convolutional Networks for Slope Deformation Prediction Based on Time-series Displacement Data

A Deep Learning Approach Using Graph Convolutional Networks for Slope Deformation Prediction Based on Time-series Displacement Data

A deep learning approach using graph convolutional networks for slope deformation prediction based on time-series displacement data

A Deep Learning Approach Using Graph Convolutional Networks for Slope Deformation Prediction Based on Time-series Displacement Data

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