No abstract

The stability of a dike is influenced strongly by its water content, by way of changes in effective stress and weight. While flow through porous media is relatively well understood, water flux in and out of a dike through a vegetated surface is not as well understood. This paper presents a numerical study of the soil–vegetation–atmosphere interaction and discusses how it influences the stability of dikes covered with grass. A crop model was used to simulate vegetation growth and infiltration in response to meteorological forcing. The PLAXIS finite-element method model was used to simulate the impact of this infiltration on hydromechanical behaviour and dike stability. Results from a 4-year analysis indicated a strong correlation between root zone water content (WC rz) and factor of safety, although the relationship is not unique. The leaf area index (LAI) was also found to have a strong, lagged correlation with the water flux into the dike. This suggests that monitoring LAI could be a useful tool to identify vulnerable locations along dikes. It is therefore proposed that vegetation and root zone water content could be used as an indication to detect vulnerable dikes in the early stage.

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.

Climatic conditions and vegetation cover influence water flux in a slope which affect the pore water pressure and self weight, hence its stability. High evapotranspiration and low precipitation rates during summer cause dry soil with low soil moisture (SM) that leads to soil shrinkage, which leads to cracking and reduced shear strength, which consequently decreases the stability of slopes. Soil re-wetting increases slope weight and exerts an additional driving force on the slope. Using Earth observation (EO) data facilitates frequent, large-scale monitoring to identify the vulnerable areas along the slopes to avoid instability. Here we study the displacement of a vegetated dike subject to SM variations under varying climatic conditions. Results show that the SM and magnitude of total displacement at a desired location are highly positively correlated without time lag. This proof-of-concept study shows that near surface displacement due to interaction with the atmosphere has a strong relation with the water availability in the slope and therefore the Factor of Safety (FoS).

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.