Real-time prediction and analysis&nbsp; of jacket platform dynamic response&nbsp;based on one-dimensional convolution and GRU network

ZHANG Zhen, CUI Chunyi, ZHANG Peng, LIU Hailong, WANG Kunpeng, LI Xue

doi:10.16411/j.cnki.issn1006-7736.2023.02.003

2023 , Vol. 49 >Issue 2: 23 - 32

DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2023.02.003

Real-time prediction and analysis of jacket platform dynamic response based on one-dimensional convolution and GRU network

Expand

(College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China)

Online published: 2023-02-21

Fold

Abstract

In order to effectively achieve a real-time prediction of jacket platforms dynamic response under wave load,the dynamic response prediction method combining one-dimensional convolutional neural network and gated recurrent composite neural network(1DCNN+GRU) was proposed. Based on the SACS program platform,the numerical calculation model of single deck four-legged jacket was established.The dynamic dynamic response state of the jecket under wave action was analyzed by using a nonlinear numerical model to obtain the structural dynamic response time course sample data,and then input into a composite neural network built by the Pytorch framework for training and testing after standardization processing.The calculation and analysis results show that the 1DCNN+GRU composite neural network prediction method has good solution accurate and stability, and each solution step length was less than the amount of forecast advance,which can achieve the dynamic dynamic response real-time prediction of offshore jacket platforms, and provie reference for relevant engineering practices.

Key words： jacket platform; GRU network; dynamic response prediction; one dimensional convolution

Cite this article

ZHANG Zhen, CUI Chunyi, ZHANG Peng, LIU Hailong, WANG Kunpeng, LI Xue . Real-time prediction and analysis of jacket platform dynamic response based on one-dimensional convolution and GRU network[J]. Journal of Dalian Maritime University, 2023 , 49(2) : 23 -32 . DOI: 10.16411/j.cnki.issn1006-7736.2023.02.003

References

[1]吴志浩,崔春义,张新程,等. 直立腿海洋平台冰激振动响应参数敏感性分析[J]. 大连海事大学学报,2021,47(4):93-99.

WU Z H, CUI C Y, ZHANG X C, et al. Sensitivity analysis of ice-induced vibration response parameters of the vertical leg offshore platform[J]. Journal of Dalian Maritime University,2021,47(4):93-99. (in Chinese)

[2]黄礼敏. 海浪中非平稳非线性舰船运动在线预报研究[D]. 哈尔滨:哈尔滨工程大学, 2016.

HUANG L M. On-line prediction of non-stationary and nonlinear ship motions at sea[D]. Harbin:Harbin Engineering University, 2016. (in Chinese)

[3]SØRENSEN P H, NØRGAARD M, RAVN O, et al. Implementation of neural network based non-linear predictive control[J]. Neurocomputing (Amsterdam), 1999,28(1): 37-51.

[4]THIRUMALAISELVI A, MOHIT V, ANANDAVALLI N, et al. Response prediction of laced steel-concrete composite beams using machine learning algorithms[J].Structural Engineering and Mechanics, 2018, 66(3):399-409.

[5]SAKARIDIS E, KARATHANASOPOULOS N, MOHRD. Machine-learning based prediction of crash response of tubular structures[J]. International Journal of Impact Engineering, 2022, 166: 104240.

[6]SAHOO D M, CHAKRAVERTY S. Functional link neural network learning for response prediction of tall shear buildings with respect to earthquake data [ J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems,2018, 48(1): 1-10.

[7]OH B K, GLISIC B, PARK S W, et al. Neural networkbased seismic response prediction model for building structures using artificial earthquakes [ J]. Journal of Sound and Vibration, 2020, 468: 115109.

[8]AHMED B, MANGALATHU S, JEON J. Seismic damage state predictions of reinforced concrete structures using stacked long short-term memory neural networks[ J ]. Journal of Building Engineering, 2022, 46:103737.

[9]ZHAO Y, DONG S, JIANG F, et al. Mooring tension prediction based on BP neural network for semi-submersible platform [ J ]. Ocean Engineering, 2021, 223:108714.

[10]WANG Z, QIAO D, YAN J, et al. A new approach to predict dynamic mooring tension using LSTM neural network based on responses of floating structure[J]. Ocean Engineering, 2022, 249: 110905.

[11]YAO J, WU W, LI S. Anomaly detection model of mooring system based on LSTM PCA method [J].Ocean Engineering, 2022, 254: 111350.

[12]GUO X, ZHANG X, TIAN X, et al. Probabilistic prediction of the heave motions of a semi-submersible by a deep learning model[J]. Ocean Engineering, 2022, 247: 110578.

[13]李昊波,肖龙飞,魏汉迪,等. 基于LSTM 网络的浮式海洋平台运动在线预报研究[J]. 船舶力学, 2021,25(5): 576-585.

LI H B, XIAO L F, WEI H D, et al. Research on online motion prediction of floating offshore platform based on LSTM network [ J]. Journal of Ship Mechanics, 2021, 25(5): 576-585. (in Chinese)

[14]张德庆,王超,杜君峰. 基于人工神经网络算法的深海浮式系统动力响应预报方法[J]. 中国造船,2021, 62(1): 123-132.

ZHANG D Q, WANG C, DU J F. Prediction method of dynamic response of deep-sea floating system based on artificial neural network algorithm[J]. Shipbuilding of China, 2021, 62(1): 123-132. (in Chinese)

[15]CHO K, MERRIENBOER B V, GULCEHRE C, et al.Learning phrase representations using RNN encoder-decoder for statistical machine translation[C] / / Proceedings of the 2014 Conference on Empirical Methods inNatural Language Processing (EMNLP). [S. l. :s. n. ],2014: 1724-1734.

Options

Outlines

模态框（Modal）标题

Abstract

Cite this article

References