In the physical modelling of coastal engineering problems, use is often made of foreshores and transition slopes to obtain the desired wave conditions - both spectral parameters and wave height distribution - at a given location. Numerical wave models can be used to predict whether the target wave conditions are met for a given physical model layout and wave forcing. The XBeach non-hydrostatic two-layer model is a computationally efficient numerical model that has been validated for spectral wave parameters, but lacks validation of the simulated wave height distributions. In this work, wave flume data with high spatial density over a sloping foreshore is used to validate the ability of this numerical model to reproduce both spectral wave parameters and wave height distributions. As part of this effort, optimal settings have been derived for the wave breaking formulation used in the numerical model, resulting in recommended values for the maxbrsteep and reformsteep parameters of 0.40 and 0.20 respectively.   From the results of the validation it is concluded that the numerical model is unsuccessful in reproducing the validation tests with 5.0% wave steepness, potentially due to the higher kph numbers on the generating model boundary. Hence, using the numerical model with values of kph ≥ 2 on the model boundary is not recommended.   The XBeach non-hydrostatic two-layer model performs much better for the 1.0% and 2.5% wave steepness tests, where the spectral wave parameters are represented well. The corresponding wave height distributions are represented reasonably well up to the point that the relative water depth gets very shallow. For shallower water, the model is expected to underestimate the higher waves in the wave height distribution. Additionally, the numerical model is shown to reproduce the wave height distribution better than a commonly used analytical formulation for wave height distributions on slopes.@en

When performing physical or numerical experiments in a wave flume, it is important to distinguish between the incident and reflected wave components. Recently wave separation methods including nonlinear effects were presented which can be applied for a large range of conditions and reduces the error in the wave separation. However, these nonlinear methods also result in a complex system of equations and require more wave gauges. In this work, physical model experiments with a high spatial resolution of wave gauges were carried out to validate different wave separation methods. All tests were analysed with a Linear method, the nonlinear method described in Eldrup and Lykke Andersen (2019) (ELA method) and a Modified ELA method. In this Modified ELA method, the model complexity depends only on the condition number of the phase difference matrix making the accuracy less dependent on wave gauge positions and noise. The results show that both Nonlinear methods are always preferred over the Linear method. A detailed analysis of the number of wave gauges shows that for the same distance between first and last gauge, 6 to 8 wave gauges are required for a converged solution independently of the wave gauge position. In an ideal situation – with an ideal number of wave gauges and ideal spacing between them – the ELA and Modified ELA methods should give nearly identical results. However, for the non-ideal conditions encountered in practice, the Modified ELA is shown to be more robust.@en

Operational wave forecasting plays an important role in ensuring safe navigation and in the prediction of tidal windows for harbour approach channels. The underlying nearshore process-based wave models need to be accurate for a wide range of different conditions, from more common mild wave conditions to the occasional high energy (storm) conditions. In this work, an innovative hybrid modelling approach is proposed to improve the accuracy of operational wave forecasts. An operational wave model is combined with a machine learning model which is trained using wave measurements within the wave model domain. This hybrid modelling approach is applied to the Dutch North Sea, covering four major harbour approach channels.

The final hybrid operational wave model results in a significant average error decrease compared to just the process-based model, amounting to 21.7% for the wave energy density and 25.3% for the wave direction. The error reduction for the spectral wave parameters is even larger, with a 33.3% smaller error in spectral wave height and a 38.8% smaller error in spectral wave period. As this approach is generically applicable to spectral wave models, it contains the potential for significant improvements in operational modelling.@en

Coastal structures are often designed to a maximum allowable wave overtopping discharge, hence accurate prediction of the amount of wave overtopping is an important issue. Both empirical formulae and neural networks are among the commonly used prediction tools. In this work, a new model for the prediction of mean wave overtopping discharge is presented using the innovative machine learning technique XGBoost. The selection of features to train the model on is carefully substantiated, including the redefinition of existing features to obtain a better model performance. Confidence intervals are derived by tuning hyperparameters and applying bootstrap resampling. The quality of the model is tested against four new physical model data sets, and a thorough quantitative comparison with existing machine learning methods and empirical overtopping formulae is presented. The XGBoost model generally outperforms other methods for the test data sets with normally incident waves. All data-driven methods show less accuracy on oblique wave data, presumably because these conditions are underrepresented in the training data. The performance of the XGBoost model is significantly improved by adding a randomly selected part of the new oblique wave cases to the training data. In the end, this new model is shown to reduce errors on all data used in this work with a factor of up to 5 compared to existing overtopping prediction methods.

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Wave overtopping is an important design criterion for coastal structures such as dikes, breakwaters and promenades. Hence, the prediction of the expected wave overtopping discharge is an important research topic. Existing prediction tools consist of empirical overtopping formulae, machine learning techniques like neural networks, and numerical models. In this paper, an innovative machine learning method-gradient boosting decision trees-is applied to the prediction of mean wave overtopping discharges. This new machine learning model is trained using the CLASH wave overtopping database. Optimizations to its performance are realized by using feature engineering and hyperparameter tuning. The model is shown to outperform an existing neural network model by reducing the error on the prediction of the CLASH database by a factor of 2.8. The model predictions follow physically realistic trends for variations of important features, and behave regularly in regions of the input parameter space with little or no data coverage.

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In coastal engineering context, the use of video imagery is widespread. Especially in field settings along sandy coasts, many types of data have been derived from video imagery, often using non-learning analysis techniques. Recent advances in the field of computer vision and deep learning allow for the automation of image segmentation. In this paper, these techniques are combined with video imagery of physical model tests, resulting in an innovative non-intrusive measurement technique. This technique is validated for three different applications: the measurement of surface elevation, wave run-up and bed level development. In addition to demonstrating its potential as an alternative for existing measurement instruments, it is shown that the added detail in the spatial or temporal domain provided by the technique can lead to new insights. Examples of this are the detailed analysis on the variability of the run-up height over the width of the flume and the spatial distribution of run-up velocities over the slope.@en

In order to improve the current aeolian modeling, a process-based model is developed by implementing biological and physical processes adapted from different existing models. The current version of the model is capable of simulating barchan and parabolic dunes and the initial stage of coastal dune formation: Embryo dunes. Potentially the model can be used to determine the influence of tidal ranges, storm frequencies, armoring, salinity and precipitation on dune building processes. This will result in a greater insight in the general behavior of coastal systems and on the other hand the model can also be used for engineering purposes.@en

Toe structures are a common feature in coastal rock armoured structures, both safeguarding the structure against scour at the toe and providing support to the armour layer. The toe structure itself is often comprised of rocks as well. Hence, generally some form of scour protection, such as a filter layer or a geotextile, is applied underneath the toe structure. Currently, there is a lack of available methods to judge the necessity of these kinds of costly scour protection measures underneath the toe structure. In this paper, a unique data set from physical model tests of scour at the toe of rock armoured structures is presented. The bed level change is measured by a laser scanner across the entirety of the sand bed, including underneath the structure. Additionally, the temporal development of the scour at the glass wall of the flume is measured using an innovative combination of video imagery and a deep learning algorithm. The sand diameter is not varied in the physical model tests. In all tests, scour was observed both in front of the structure and underneath the structure. Two expressions have been derived that give estimates for both kinds of scour, and thus can give insight into the necessity of scour protection underneath the toe structure.

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In this work, a CFD model (OpenFOAM) is extended to allow for the exchange of sediment between the inside of porous structures and the outside. This exploratory extended model is applied to simulate the morphological change around and under toe structures, as are often used in rock armoured coastal structures. Both the dimensions of the toe structure and the hydraulic loading are varied to investigate their effect on the simulated scour.

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