Empirical model for the prediction of lateral stability coefficient for un-trenched submarine pipes based on self-evolving neural network (SEANN)

• Published in: Ocean engineering Vol. 72; pp. 167 - 175
• Main Authors: Jeng, D.-S., Ismail, A., Zhang, L.L., Zhang, J.-S.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.11.2013; Elsevier
• Subjects: Activation; Applied sciences; Artificial neural network; Buildings. Public works; Coefficients; Empirical analysis; Exact sciences and technology; Hydraulic constructions; Lateral stability; Mathematical models; Neural networks; Offshore structure (platforms, tanks, etc.); Pipe; Pipelines; Sea beds; Self-evolving network; Soil (material); Submarine pipeline; Submarine pipeline; Lateral stability; Artificial neural network; Self-evolving network; Underwater pipeline; Neural network; Empirical model; Modeling; Ocean floors; Slope; Numerical simulation; Structure soil interaction; Offshore structure

The breakout failure mechanism of un-trenched submarine pipelines subject to current-induced hydrodynamic loading involves complex interactions between the pipe and the surrounding soil. Methods commonly used in conventional stability analyses of pipelines do not adequately consider several importan...