A new process-based, wave-resolving, 2DH circulation model for the evolution of natural sand bars: The role of nearbed dynamics and suspended sediment transport

We study the migration of natural sand bars that evolve in the nearshore by means of a new process-based waveresolving, 2DH circulation model. In order to perform reliable and accurate computations, the robust Nonlinear Shallow Water Equations (NSWEs) hydro-morphodynamic solver of Brocchini et al. (2001) and Postacchini et al. (2012) implements a detailed description of the Bottom Boundary Layer (BBL) dynamics and a new predictor for the Suspended Sediment Transport (SST) based on the solution of a Depth-Averaged Advection-Diffusion Equation (DAADE) for the sediment concentration. The robustness and accuracy of the enhanced model are validated against literature theoretical, experimental, and numerical results, all comparisons highlighting good performances and clarifying the role of both BBL and SST contributions, the former one having a larger positive influence than the latter one on the results. Both original and enhanced models are, then, used to predict the evolution of the sand bar system that characterizes the nearshore of Senigallia (AN). The analysis leverages the field observations collected at such a site by means of the Sena Gallica Speculator video-monitoring system. Modeling of the storm-forced sand bar migration patterns reveals that: 1) the enhanced model can adequately reproduce the seaward migration of the sand bars of the system; 2) the process of shoreline retreat in coincidence with the generation of a new-born bar is well described; 3) inclusion of the BBL improves quantitative prediction of the bar crest migration; 4) the SST, beyond improving the prediction of the bar crest location, induces some smoothing of the bar profile, in line with the literature findings of SST being a stabilizing factor for the bar emergence

Prediction of non-breaking wave induced scour depth at the trunk section of breakwaters using Genetic Programming and Artificial Neural Networks

Scour may act as a threat to coastal structures stability and reduce their functionality. Thus, protection against scour can guarantee these structures' intended performance, which can be achieved by the accurate prediction of the maximum scour depth. Since the hydrodynamics of scour is very complex, existing formulas cannot produce good predictions. Therefore, in this paper, Genetic Programming (GP) and Artificial Neural Networks (ANNs) have been used to predict the maximum scour depth at breakwaters due to non-breaking waves (S-max/H-nb). The models have been built using the relative water depth at the toe (h(ioe)/L-nb), the Shields parameter (theta), the non-breaking wave steepness (H-nb/L-nb), and the reflection coefficient (Cr), where in the case of irregular waves, H-nb=H-rms, T-na=T-perth and L-nb is the wavelength associated with the peak period (L-nb=L-p). 95 experimental datasets gathered from published literature on small-scale experiments have been used to develop the GP and ANNs models. The results indicate that the developed models perform significantly better than the empirical formulas derived from the mentioned experiments. The GP model is to be preferred, because it performed marginally better than the ANNs model and also produced an accurate and physically-sound equation for the prediction of the maximum scour depth. Furthermore, the average percentage change (APC) of input parameters in the GP and ANNs models shows that the maximum scour depth dependence on the reflection coefficient is larger than that of other input parameters

Prediction of non-breaking wave induced scour depth at the trunk section of breakwaters using Genetic Programming and Artificial Neural Networks

International audienceScour may act as a threat to coastal structures stability and reduce their functionality. Thus, protection against scour can guarantee these structures’ intended performance, which can be achieved by the accurate prediction of the maximum scour depth. Since the hydrodynamics of scour is very complex, existing formulas cannot produce good predictions. Therefore, in this paper, Genetic Programming (GP) and Artificial Neural Networks (ANNs) have been used to predict the maximum scour depth at breakwaters due to non-breaking waves (Smax/Hnb). The models have been built using the relative water depth at the toe (htoe/Lnb), the Shields parameter (θ), the non-breaking wave steepness (Hnb/Lnb), and the reflection coefficient (Cr), where in the case of irregular waves, Hnb=Hrms, Tnb=Tpeak and Lnb is the wavelength associated with the peak period (Lnb=Lp). 95 experimental datasets gathered from published literature on small-scale experiments have been used to develop the GP and ANNs models. The results indicate that the developed models perform significantly better than the empirical formulas derived from the mentioned experiments. The GP model is to be preferred, because it performed marginally better than the ANNs model and also produced an accur