Pipeline scour rates prediction-based model utilizing a multilayer perceptron-colliding body algorithm

Abstract

In this research, the advanced multilayer perceptron (MLP) models are utilized to predict the free rate of expansion that usually occurs around the pipeline (PL) because of waves. The MLP model was structured by integrating it with three optimization algorithms: particle swarm optimization (PSO), whale algorithm (WA), and colliding bodies' optimization (CBO). The sediment size, wave characteristics, and PL geometry were used as the inputs for the applied models. Moreover, the scour rate, vertical scour rate along the pipeline, and scour rate at both right and left sides of the pipeline were predicted as the model outputs. Results of the three suggested models, MLP-CBO, MLP-WA, and MLP-PSO, for both testing and training sessions were assessed based on different statistical indices. The results indicated that the MLP-CBO model performed better in comparison to the MLP-PSO, MLP-WA, regression, and empirical models. The MLP-CBO can be used as a powerful soft-computing model for predictions.