Artificial Intelligence and Rubble-Mound Breakwater Stability
Gregorio Iglesias Rodriguez (University of Santiago de Compostela, Spain), Alberte Castro Ponte (University of Santiago de Compostela, Spain), Rodrigo Carballo Sanchez (University of Santiago de Compostela, Spain) and Miguel Ángel Losada Rodriguez (University of Granada, Spain)
Copyright: © 2009
Breakwaters are coastal structures constructed to shelter a harbour basin from waves. There are two main types: rubble-mound breakwaters, consisting of various layers of stones or concrete pieces of different sizes (weights), making up a porous mound; and vertical breakwaters, impermeable and monolythic, habitually composed of concrete caissons. This article deals with rubble-mound breakwaters. A typical rubble-mound breakwater consists of an armour layer, a filter layer and a core. For the breakwater to be stable, the armour layer units (stones or concrete pieces) must not be removed by wave action. Stability is basically achieved by weight. Certain types of concrete pieces are capable of achieving a high degree of interlocking, which contributes to stability by impeding the removal of a single unit. The forces that an armour unit must withstand under wave action depend on the hydrodynamics on the breakwater slope, which are extremely complex due to wave breaking and the porous nature of the structure. A detailed description of the flow has not been achieved until now, and it is unclear whether it will be in the future in view of the turbulent phenomena involved. Therefore the instantaneous force exerted on an armour unit is not, at least for the time being, amenable to determination by means of a numerical model of the flow. For this reason, empirical formulations are used in rubble-mound design, calibrated on the basis of laboratory tests of model structures. However, these formulations cannot take into account all the aspects affecting the stability, mainly because the inherent complexity of the problem does not lend itself to a simple treatment. Consequently the empirical formulations are used as a predesign tool, and physical model tests in a wave flume of the particular design in question under the pertinent sea climate conditions are de rigueur, except for minor structures. The physical model tests naturally integrate all the complexity of the problem. Their drawback lies in that they are expensive and time consuming. In this article, Artificial Neural Networks are trained and tested with the results of stability tests carried out on a model breakwater. They are shown to reproduce very closely the behaviour of the physical model in the wave flume. Thus an ANN model, if trained and tested with sufficient data, may be used in lieu of the physical model tests. A virtual laboratory of this kind will save time and money with respect to the conventional procedure.
Physical Model And Ann Model
The Artificial Neural Networks were trained and tested on the basis of laboratory tests carried out in a wave flume of the CITEEC Laboratory, University of La Coruña. The flume section is 4 m wide and 0.8 m high, with a length of 33 m (Figure 1). Waves are generated by means of a piston-type paddle, controlled by an Active Absorption System (AWACS) which ensures that the waves reflected by the model are absorbed at the paddle.
Key Terms in this Chapter
Reflection: The process by which the energy of the incoming waves is returned seaward.
Artificial Neural Networks: Interconnected set of many simple processing units, commonly called neurons, that use a mathematical model representing an input/output relation.
Armour Layer: Outer layer of a rubble-mound breakwater, consisting of heavy stones or concrete blocks.
Significant Wave Height: In wave record analysis, the average height of the highest one-third of a selected number of waves.
Breakwater: Coastal structure built for sheltering an area from waves, usually for loading or unloading vessels.
Armour Damage: Extraction of stones or concrete units from the armour layer by wave action.
Backpropagation algorithm: Supervised learning technique used by ANNs that iteratively modifies the weights of the connections of the network so the error given by the network after the comparison of the outputs with the desired one decreases.