Journal of Waterway, Port, Coastal, and Ocean Engineering

Loading from ocean waves propagating in shallow water will normally set some of the bottom sediment in motion, resulting in well-known and far-reaching consequences. In this study, we highlight the role of wave pressure in some of the physical processes taking place at and near the seafloor interface during the propagation of a water wave above. Poroelasticity is used to model the strain in the seabed. Saffman’s slip boundary condition at a fixed porous boundary is extended for a porous bed that is both deformable as well as erodible. The granular flow of the fluidized sediment on the seafloor is modeled as a viscoelastic fluid, with a frequency-dependent complex-viscosity coefficient. A Coulomb-type failure criterion is employed to quantify the magnitude of fluidization under the combined wave shear-and-pressure loading. Nonlinear boundary-layer equations are then solved for the motion of the fluidized sediment and hence provide an estimation of bedload transport by water waves.

In this paper we describe laboratory experiments and numerical simulations of the impact between a rigid body and water. The rigid body consists of a weighted rectangular box that slides down a curved ramp to enter the water at an angle to the horizontal of approximately 15°. As the box enters the water, a vigorous spray projects fluid beyond the box, then, as the water is heaved up, it falls back on the upper surface of the box and initiates a solitary wave. The experiments cover a wide range of impact velocities and three water depths in the main body of the tank. The experiments have been simulated using the Lagrangian particle method smoothed particle hydrodynamics (SPH).

Using the elliptic mild-slope equation to resolve wave fields in large coastal areas requires enormous computer resources, thus imposing a great restriction on the applicability of this equation for practical engineering problems. An improved numerical model based on the mild slope RCPWAVE model has been developed for computing wave refraction and diffraction in a large coastal area with complex coastline near the Pearl River estuary, in China. An operator splitting method is employed to solve the wave action equation, in which the advection terms are resolved by the Eulerian-Lagrangian method to increase numerical stability and the other terms are discretized by the implicit finite-element method to fit complex coastline geometries. A stable and efficient nominal-time finite-node method is proposed to solve the nonlinear irrotational wave number equation for wave directions. Numerical tests on wave propagation proved that the present model has significant improvements in model stability and efficiency over the RCPWAVE model. Different swell transformation scenarios in the Pearl River estuary have been simulated by the model. For such a large and complex estuarine region, the model simulated the wave distributions reasonably well, with good efficiency, and it also produced results that closely matched the field measurements collected at two wave gauges.

This paper describes the development of a new parameter to characterize dune vulnerability to storm-induced erosion. Existing indicators of dune erosion vulnerability are examined, including expected cross-sectional erosion values calculated using storm characteristics. We extract a series of 110 pre- and poststorm profiles at cross-shore transects, spaced at approximately 300 m alongshore on digital terrain models of a North Carolina barrier island. Dune failure and survival are designated based on a percentage of the cross-sectional area eroded—50% or greater erosion indicates failure, less than 50% erosion, survival. Crest height does not prove to be an effective predictor of dune vulnerability. Existing cross-sectional area based parameters show some success in predicting erosion vulnerability. We improve the dune failure and survival prediction success rate using a new parameter, a surrogate moment of inertia, the erosion resistance.

The U.S. Army Corps of Engineers maintains a database of cost information for competitively bid dredging projects. These data were used to construct linear regression models and radial-basis-function neural networks to predict the completed cost of the dredging projects. The stepwise linear regression procedure was used to construct equations to predict the completed cost based on input of the low bid, government cost estimates, and estimated project-dredging quantities. A data transformation using the natural logarithm enhanced the linear relationships between the variables. An exponential relationship between the low bid and completed cost indicated that large dredging projects could be completed for less than the bid amount. The variables used as inputs to the neural networks were the low bid, the government estimate, estimated quantity, the type of dredge, the method of dredged material disposal, the number of bidders, and the class of work. The addition of categorical variables like the type of dredging and disposal method did not improve the predictive performance of the neural network. The best neural network model was able to predict 40.4% of the test set projects within 10% of the actual cost. The best regression model predicted 51.4% of the projects within 10% of the actual cost.

The cost to decommission offshore structures in the Gulf of Mexico is examined based on 60 projects managed by Twachtman, Synder, & Byrd, Inc., over the years 1991–2001. Cost functions are established for each stage of decommissioning, including well plugging and abandonment, structure preparation, pipeline abandonment, structure removal, and site clearance and verification. The component cost of decommissioning is analyzed statistically, and regression models are empirically derived using various descriptor variables. A candid discussion of the limitations of analysis is also presented. This paper represents the first empirical study to construct decommissioning cost functions for the Gulf of Mexico region based on actual job data.