Design of rubble-mound breakwaters using M5 ′ machine learning method

Predicting the stability of armor blocks of breakwaters and revetments is a very important issue in coastal and ocean engineering. Recently, soft computing tools such as artificial neural networks and fuzzy logic have been used to predict the stability number of armor blocks. However, these tools are not as transparent as empirical formulas. This study presents another soft computing approach, i.e. model trees for predicting the stability number of armor blocks. The main advantage of model trees is that, unlike the other data learning tools, they are easier to use and more importantly they represent understandable mathematical rules. A total of 579 experimental test data from Van der Meer 1988 are used for developing the model. The conventional governing parameters were selected as the input variables and the obtained results were compared with those of measurements, empirical and soft computing models. Using statistical measures, it was shown that the developed models are more accurate than previous empirical and soft computing models. Furthermore, some simple rules are given for armor blocks’ design.