Genetic algorithms based logic-driven fuzzy neural networks for stability assessment of rubble-mound breakwaters

This study focuses on the further development of fuzzy neural network (‘FNN’) models for the prediction of stability numbers for the design of rubble mound breakwaters. It introduces two new FNN models namely: (i) the genetic algorithm-based fuzzy neural network (‘GA-FNN’); and (ii) the hybrid genetic algorithm-based fuzzy neural network (‘HGA-FNN’). GA-FNN uses a standard genetic algorithm (‘GA’) to optimise both its structure and parameters. HGA-FNN is the extension of GA-FNN; however, a conditional local search method is involved. The results show that HGA-FNN has a better predictive performance than GA-FNN and that it has good potential in terms of stability assessments of coastal structures.

► Two new fuzzy neural network models (GA-FNN and HGA-FNN) are developed. ► Both models predict the stability numbers of rubble mound breakwaters. ► The predictive performance of the HGA-FNN model is better than that of the GA-FNN model. ► HGA-FNN has better potential for stability assessments of rubble mound breakwaters.