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.

Exploring 3D Wave-Induced Scouring Patterns around Subsea Pipelines with Artificial Intelligence Techniques

“…The AI models considered benefit from two intrinsic advantages: The first merit is to drive regression-based equations along with highlighting the physical meaning of the experimental observations; the second remarkable merit is associated with the upscaling of datasets. In this way, the dimensionless structure of equations given by the proposed AI models would be applicable at various scales from laboratory to field studies (e.g., Parsaie et al [18], Najafzadeh and Saberi-Movahed [26], Ehteram et al [27], and Sharafati et al [30]).…”

“…From their study, GMDH-GEP indicated relatively better performance in the prediction of three-dimensional free spans than GEP, GMDH, and empirical equations. Moreover, Ehteram et al [27] applied new improvements of ANN, by Colliding Bodies' Optimization (CBO), to model the three-dimensional nature of scour below seabed pipelines. Ultimately, they concluded that the improvement of the application of CBO into ANN resulted in more accurate predictions than PSO and the Whale Algorithm (WA).…”

“…However, the chief limitations of the above investigations are the regression-basedequations, given by GMDH-GEP (Najafzadeh and Saberi-Movahed [26]), are their tree-like structures with remarkable complexity, although equations are sufficiently accurate. On the downside, the use of an improved ANN model by Ehteram et al [27] acts as a black box, which has no potential of being consistent with experimental observations.…”

“…In fact, Artificial Neural Networks (ANNs) (Azamathulla and Zakaria [15], Etemad-Shahidi et al [16]), Adaptive Neuro-Fuzzy Inference Systems (ANFIS) (Zanganeh et al [17]), Support Vector Machine (SVM) (Parsaie et al [18]), Classification and Regression Tree (CART) (Etemad-Shahidi et al [16], Yasa and Etemad-Shahidi [19]), Linear Genetic Programming (LGP), Gene-Expression Programming (GEP) (Azamathulla and Yusoff [20], Najafzadeh and Sarkamaryan [21]), Genetic Programming (GP) (Azamathulla and Ghani [22]), Group Method of Data Handling (GMDH) (Najafzadeh et al [23], Najafzadeh et al [24]), Multivariate Adaptive Regression Splines (MARS) (Haghiabi [25]), and Evolutionary Polynomial Regression (EPR) (Najafzadeh and Sarkamaryan [21]) proved to be successful applications. In the case of scour rates prediction, there have been a few endeavors to estimate threedimensional free spans via GMDH and ANN models below a pipeline exposed to regular waves (Najafzadeh and Saberi-Movahed [26], Ehteram et al [27]).…”

Application of Deep Learning Method for Daily Streamflow Time-Series Prediction: A Case Study of the Kowmung River at Cedar Ford, Australia

“…Over the last decades, the hydrologists have popularly used the soft computing methods for streamflow modeling. Since Artificial Neural Network (ANN) has ability to model linear and non-linear systems even without making any assumption, the models of ANN were widely used in various water science subjects [2][3][4][5][6].…”