Genetic programming for the prediction of berm breakwaters recession

The response of berm breakwaters to wave forces has been examined with rebuild and cumulative experiments. In rebuild experiments, the breakwaters were reconstructed after each test, whereas in cumulative experiments the structural damages were examined at the end of the experiment. This study presents a new method to investigate the berm breakwaters recession considering datasets collected of both types of experiments. Cumu-lative experimental results were converted to their equivalent rebuild experimental results by modifying the number of waves for the reported damage. After homogenizing the data, the datasets were divided into the train, validation, and test subsets. The data were analyzed using the Multi-Objective Genetic Programming (MOGP) approach, and a prediction model was created to evaluate the berm breakwater recession. The results obtained from the MOGP model were compared to outcomes computed using implicit formulas available in the literature showing that the MOGP model is accurate (R2 = 0.911 and RMSE = 0.111) with a relatively broader applica-bility range. The impact of each input parameter on the berm breakwater recession was examined using para-metric and sensitivity analyses. The stability number was the most important parameter impacting the damage on the coastal structure. The results are in line with findings reported in previous studies.

Automated summary

The response of berm breakwaters to wave forces was investigated through rebuild and cumulative experiments. A new method was proposed to study the berm breakwaters recession by considering datasets collected from both types of experiments. The Multi-Objective Genetic Programming approach was used to analyze the data and create a prediction model, which showed reliable results compared to implicit formulas in the literature.