The impact of evaporation induced cracks and precipitation on temporal slope stability

Jamalinia, Elahe; Steele‐Dunne, Susan

doi:10.1016/j.compgeo.2020.103506

Cited by 46 publications

(28 citation statements)

References 21 publications

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“…Our recent studies [36,41] show that transient surface displacement and vegetation condition have strong correlations with the slope FoS, due to their correlation with the moisture content of the slope. In those numerical studies, the meteorological data combined with soil parameters were used as input for the model to estimate the change in FoS and the non-linear hydro-mechanical behaviour of a dike under various weather and vegetation conditions.…”

Section: Introductionmentioning

confidence: 87%

“…Recently, an integrated crop-geotechnical model has been proposed by the authors of this paper for the numerical simulation of dikes and dike FoS to account for hydrometeorological conditions and the influence of vegetation [36,41]. Here, the same model is used to provide detailed hydro-geomechanical and safety analysis of an idealised dike in order to create a 10-year-long time series of synthetic data of the dike behaviour.…”

Section: Methodsmentioning

confidence: 99%

“…In recent research [36], the two models (LINGRA and PLAXIS) were coupled together to assess the effect of variable climatic and vegetation conditions on dike stability. As evaporation-induced cracks alter the water balance in a dike by increasing flow through the cracks and at the same time reducing the shear strength of the root zone, the model was further modified to include the effect of surface cracks [41]. Here, it is assumed that the cracks do not seal (close) in the wet periods, but only expand during unprecedented dry conditions.…”

Section: Integrated Crop-geotechnical Modelmentioning

confidence: 99%

“…An example dike geometry is used in the numerical study which, to a large extent, resembles a regional (secondary) dike in Amsterdam, the Netherlands [2]. The same example dike is also used in [36,41,42]. This dike ( Figure 4) has a relatively steep slope on the lefthand side and a gentle slope on the right-hand side and is permanently covered by grass.…”

Section: Case Study 231 Numerical Modelmentioning

confidence: 99%

See 3 more Smart Citations

A Data-Driven Surrogate Approach for the Temporal Stability Forecasting of Vegetation Covered Dikes

Jamalinia

Tehrani

Steele‐Dunne

2021

Water

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Climatic conditions and vegetation cover influence water flux in a dike, and potentially the dike stability. A comprehensive numerical simulation is computationally too expensive to be used for the near real-time analysis of a dike network. Therefore, this study investigates a random forest (RF) regressor to build a data-driven surrogate for a numerical model to forecast the temporal macro-stability of dikes. To that end, daily inputs and outputs of a ten-year coupled numerical simulation of an idealised dike (2009–2019) are used to create a synthetic data set, comprising features that can be observed from a dike surface, with the calculated factor of safety (FoS) as the target variable. The data set before 2018 is split into training and testing sets to build and train the RF. The predicted FoS is strongly correlated with the numerical FoS for data that belong to the test set (before 2018). However, the trained model shows lower performance for data in the evaluation set (after 2018) if further surface cracking occurs. This proof-of-concept shows that a data-driven surrogate can be used to determine dike stability for conditions similar to the training data, which could be used to identify vulnerable locations in a dike network for further examination.

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Section: Introductionmentioning

confidence: 87%

Section: Methodsmentioning

confidence: 99%

Section: Integrated Crop-geotechnical Modelmentioning

confidence: 99%

Section: Case Study 231 Numerical Modelmentioning

confidence: 99%

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A Data-Driven Surrogate Approach for the Temporal Stability Forecasting of Vegetation Covered Dikes

Jamalinia

Tehrani

Steele‐Dunne

2021

Water

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“…However, the accuracy of the LEM is commonly affected to some extent because of the assumption on slip surface and interslice forces. Also, the numerical calculation method based on the theory of elasticity and plasticity requires the selection of a well-fitted constitutive model that can reflect the mechanical behavior of soil and rocks in slopes accurately, which is a difficult task for engineers and researchers [10][11][12][13]. Moreover, the interactions among the factors that affect slope stability are complex and multifactorial, thereby resulting in the difficult evaluation of the real slope stability based on safety factors.…”

Section: Introductionmentioning

confidence: 99%

Slope stability evaluation using backpropagation neural networks and multivariate adaptive regression splines

Liao¹,

Liao

2020

Open Geosciences

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Slope stability assessment is a critical concern in construction projects. This study explores the use of multivariate adaptive regression splines (MARS) to capture the intrinsic nonlinear and multidimensional relationship among the parameters that are associated with the evaluation of slope stability. A comparative study of machine learning solutions for slope stability assessment that relied on backpropagation neural network (BPNN) and MARS was conducted. One data set with actual slope collapse events was utilized for model development and to compare the performance of BPNN and MARS. Research results suggest that BPNN and MARS models can model the relationship between the safety factor and the slope parameters. Also, the MARS model has the advantages of computational efficiency and easy interpretation.

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Predicting Rainfall Induced Slope Stability Using Random Forest Regression and Synthetic Data

Jamalinia

Tehrani

Steele‐Dunne

2020

Understanding and Reducing Landslide Disaster Risk

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Water fluxes in slopes are affected by climatic conditions and vegetation cover, which influence the effective stress and stability. The vegetation cover is the intermediate layer between the atmosphere and the slope surface that alter water balance in the slope through evapotranspiration and leaf interception. This paper studies the data-driven approach for predicting the macro stability of an example grass-covered dike based on actual data and also synthetic data provided by numerical modelling. Two numerical models are integrated in this study. The water balance in the root zone is simulated through a crop model, whereas the hydro-mechanical and safety analysis of the example dike is done using a two-dimensional Finite Element model. The considered period for these analyses is 10 years (3650 daily instances) which will be used to generate a time-series dataset for a secondary dike in the Netherlands. The features included in the dataset are parameters that (i) have a meaningful relationship with the dike Factor of safety (FoS), and (ii) can be observed using satellite remote sensing. The output dataset is used to train a Random Forest regressor as a supervised Machine Learning (ML) algorithm. The results of this proof-of-concept study indicate a strong correlation between the numerically estimated FoS and the ML-predicted one. Therefore, it can be suggested that the utilized parameters can be used in a data-driven predictive tool to identify vulnerable zones along a dike without a need for running expensive numerical simulations.

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The impact of evaporation induced cracks and precipitation on temporal slope stability

Cited by 46 publications

References 21 publications

A Data-Driven Surrogate Approach for the Temporal Stability Forecasting of Vegetation Covered Dikes

A Data-Driven Surrogate Approach for the Temporal Stability Forecasting of Vegetation Covered Dikes

Slope stability evaluation using backpropagation neural networks and multivariate adaptive regression splines

Predicting Rainfall Induced Slope Stability Using Random Forest Regression and Synthetic Data

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