Approximation of the maximum dynamic stress range in steel catenary risers using artificial neural networks

Abstract Adequate assessment of fatigue damage in steel catenary risers (SCRs) is essential, and usually evaluated with time consuming numerical analyses. Simplified design strategies would improve the efficiency of the screening tasks in the early design stages. As part of on-going research aiming to define a simplified fatigue design procedure for SCRs in the touchdown zone (TDZ), the sensitivity of fatigue damage to various parameters is explored using a large database (>40,000 cases). An approximation of the maximum stress range in the TDZ is established using several artificial neural networks and predicts well the fatigue life of selected example SCRs.

[1]  D. Hammerstrom,et al.  Working with neural networks , 1993, IEEE Spectrum.

[2]  Suat Tanaydin.  Robust Design and Analysis for Quality Engineering , 1996 .

[3]  Mark Randolph,et al.  Artificial neural network development for stress analysis of steel catenary risers: Sensitivity study and approximation of static stress range , 2014 .

[4]  Eduardo Nobre Lages,et al.  Compliant vertical access riser assessment: DOE analysis and dynamic response optimization , 2013 .

[5]  C. Bridge,et al.  Effects of seabed interaction on steel catenary risers , 2005 .

[6]  Mark Randolph,et al.  Dimensionless groups governing response of steel catenary risers , 2013 .

[7]  Jiangpeng Shu,et al.  The application of a damage detection method using Artificial Neural Network and train-induced vibrations on a simplified railway bridge model , 2013 .

[8]  Massimo Callegari,et al.  Simple analytical models for the J-lay problem , 2005 .

[9]  Lucile M. Quéau,et al.  Estimating the fatigue damage of steel catenary risers in the touchdown zone , 2015 .

[10]  Basim B. Mekha,et al.  New Frontiers in the Design of Steel Catenary Risers for Floating Production Systems , 2001 .

[11]  Daniel Karunakaran,et al.  A parametric design study for a semi/SCR system in Northern North Sea , 2008 .

[12]  Holger R. Maier,et al.  DATA DIVISION FOR DEVELOPING NEURAL NETWORKS APPLIED TO GEOTECHNICAL ENGINEERING , 2004 .

[13]  Shan Huang,et al.  Trenching effects on structural safety assessment of integrated riser/semisubmersible in cohesive soil , 2014 .

[14]  W. T. Yeung,et al.  Damage detection in bridges using neural networks for pattern recognition of vibration signatures , 2005 .

[15]  Mark Randolph,et al.  Dynamic amplification factors for response analysis of steel catenary risers at touch down areas , 2011 .

[16]  Ibrahim H. Guzelbey,et al.  Explicit formulation of SIF using neural networks for opening mode of fracture , 2007 .

[17]  Minoo H. Patel,et al.  Review of flexible riser modelling and analysis techniques , 1995 .