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

Quéau, Lucile M.; Kimiaei, Mehrdad; Randolph, Mark

doi:10.1016/j.engstruct.2015.02.025

Cited by 14 publications

(4 citation statements)

References 13 publications

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“…The eleven key parameters were simplified to six input variables of FSHM-MLP considering the following issues: (1) They have physical meanings in engineering, and thus they are simple and easy to use. (2) They are normalized to be dimensionless (Quéau et al, 2014, 2015); (3) They together include all of the 11 key parameters. (4) They are weakly correlated.…”

Section: Methods For Predicting the Acbcmentioning

confidence: 99%

An advanced method for predicting the axial compressive bearing capacity of fiber-reinforced polymer-strengthened hollow section metal columns

Cao

Zhang

et al. 2023

Advances in Structural Engineering

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Successful experimental and theoretical studies have promoted fiber-reinforced polymer (FRP) as an innovative strengthening material for metal columns in structural engineering, highlighting the necessity for an effective and reliable calculation method for such strengthened members. Here, based on existing experimental data, an advanced method using an artificial neural network (ANN) is developed to predict the axial compression bearing capacity (ACBC) of FRP-strengthened hollow section metal (FSHM) columns, where FRP strengthening includes wrapping/bonding FRP sheets/laminates; this method considers realistic conditions, incorporates a variety of parameters, and produces accurate and fast predictions. First, a multilayer perceptron (MLP) neural network was applied. By studying the optimization algorithm, learning rate, hidden vector and activation function, an optimal ANN model was established, which yielded better predictive accuracies and safety than existing calculation methods. Using the validated model, an prediction program was developed that promptly generated predictions of ACBC with a high reliability. Finally, a sensitivity analysis was performed to further assess the effects of key parameters on the behavior of FSHM columns to provide practical recommendations for their design.

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Section: Methods For Predicting the Acbcmentioning

confidence: 99%

An advanced method for predicting the axial compressive bearing capacity of fiber-reinforced polymer-strengthened hollow section metal columns

Cao

Zhang

et al. 2023

Advances in Structural Engineering

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“…Thus, the need for the application of Dynamic Amplification Factor (DAF), which simplifies structural dynamic analyses of linear nature. Its applications include the DAF for steel catenary risers (Quéau et al 2011;Quéau et al 2015), cranes, moving vehicles, buildings, trains and bridges. Another challenge is the need for proper coupling analysis on integral CALM buoy hose systems.…”

Section: Introductionmentioning

confidence: 99%

Strength of submarine hoses in Chinese-lantern configuration from hydrodynamic loads on CALM buoy

et al. 2019

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Catenary Anchor Leg Moorings (CALM) buoys are offshore structures that have been used for offloading, loading and discharge purposes. In this study, dynamic analysis is carried out on the submarine hoses attached to a CALM buoy and moored by six mooring lines in a water depth of 23.0m. Two submarine hose-strings in Chinese-lantern configuration are attached underneath the buoy. Three environmental conditions are considered, representing West Africa Sea, North Sea and Gulf of Mexico (GoM), respectively. Hydrodynamic simulation using ANSYS AQWA is first conducted to determine response amplitude operators (RAOs) of the buoy. Coupled dynamic models, where both buoys and hoses are included, are developed using Orcaflex. Parametric studies are conducted to investigate the effects of hose hydrodynamic loads and flow angles on the structural behaviour of the hoses, including bending moments, effective tension and minimum bend radius. From the study, a guidance dynamic amplitude factor of 2.0 considering hydrodynamic loads on hose DAFhose is proposed.

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“…Li et al 27 used various ANN models to evaluate the fatigue damage of catenary mooring lines. Quéau et al 28 conducted a fatigue damage assessment of steel catenary risers based on an approximation of the maximum dynamic stress range using ANNs. Li and Choung 29 carried out fatigue damage analysis for floating offshore wind turbine mooring lines using the ANN approach.…”

Section: Introductionmentioning

confidence: 99%

Reliability analysis of mooring lines for floating structures using ANN-BN inference

Zhao

Dong

Jiang

2020

Proceedings of the Institution of Mechanical Engineers, Part M:

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The harsh marine environment is a significant threat to the safety of floating structure systems. To address this, mooring systems have seen widespread application as an important component in the stabilization of floating structures. This article proposes a methodology to assess the reliability of mooring lines under given extreme environmental conditions based on artificial neural network–Bayesian network inference. Different types of artificial neural networks, including radial basis function neural networks and back propagation neural networks, are adopted to predict the extreme response of mooring lines according to a series of measured environmental data. A failure database under extreme sea conditions is then established in accordance with the failure criterion of mooring systems. There is a failure of mooring lines when the maximum tension exceeds the allowable breaking strength. Finally, the reliability analysis of moored floating structures under different load directions is conducted using Bayesian networks. To demonstrate the proposed methodology, the failure probability of a sample semi-submersible platform at a water depth of 1500 m is estimated. This approach utilizes artificial neural networks’ capacity for calculation efficiency and validates artificial neural networks for the response prediction of floating structures. Furthermore, it can also be employed to estimate the failure probability of other complex floating structures.

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

Cited by 14 publications

References 13 publications

An advanced method for predicting the axial compressive bearing capacity of fiber-reinforced polymer-strengthened hollow section metal columns

An advanced method for predicting the axial compressive bearing capacity of fiber-reinforced polymer-strengthened hollow section metal columns

Strength of submarine hoses in Chinese-lantern configuration from hydrodynamic loads on CALM buoy

Reliability analysis of mooring lines for floating structures using ANN-BN inference

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