Application of nature-inspired optimization algorithms to ANFIS model to predict wave-induced scour depth around pipelines

Author:

Sharafati Ahmad1,Tafarojnoruz Ali2,Motta Davide3,Yaseen Zaher Mundher4

Affiliation:

1. Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam and Faculty of Civil Engineering, Duy Tan University, Da Nang 550000, Vietnam and Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2. Dipartimento di Ingegneria Civile, Università della Calabria, Cubo 42B, Rende, Italy

3. Department of Mechanical and Construction Engineering, Northumbria University, Wynne Jones Building, Newcastle upon Tyne NE1 8ST, UK

4. Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Min City, Vietnam

Abstract

Abstract Wave-induced scour depth below pipelines is a physically complex phenomenon, whose reliable prediction may be challenging for pipeline designers. This study shows the application of adaptive neuro-fuzzy inference system (ANFIS) incorporated with particle swarm optimization , ant colony (), differential evolution and genetic algorithm () and assesses the scour depth prediction performance and associated uncertainty in different scour conditions including live-bed and clear-water. To this end, the non-dimensional parameters Shields number (), Keulegan–Carpenter number () and embedded depth to diameter of pipe ratio () are considered as prediction variables. Results indicate that the model ( and ) is the most accurate predictive model in both scour conditions when all three mentioned non-dimensional input parameters are included. Besides, the model shows a better prediction performance than recently developed models. Based on the uncertainty analysis results, the prediction of scour depth is characterized by larger uncertainty in the clear-water condition, associated with both model structure and input variable combination, than in live-bed condition. Furthermore, the uncertainty in scour depth prediction for both live-bed and clear-water conditions is due more to the input variable combination than it is due to the model structure .

Publisher

IWA Publishing

Subject

Atmospheric Science,Geotechnical Engineering and Engineering Geology,Civil and Structural Engineering,Water Science and Technology

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