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Structural Analysis and Failure Prevention in Offshore Engineering
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Structural Analysis and Failure Prevention in Offshore Engineering

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Coastal Engineering".

Deadline for manuscript submissions: closed (30 January 2025) | Viewed by 6537

Special Issue Editors

Prof. Dr. Selda Oterkus

E-Mail Website1 Website2
Guest Editor
Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow G4 0LZ, UK
Interests: offshore and subsea structures; fluid structure interaction; damage modelling; multiphysics analysis; composite structures; structural health monitoring; corrosion; fatigue
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Erkan Oterkus

E-Mail Website
Guest Editor
Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, 100 Montrose Street, Glasgow G4 0LZ, UK
Interests: digital twins; structural health monitoring; structural analysis of offshore renewable energy devices; additive manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Offshore engineering is an important field of engineering. In order to continue our daily activities and industrial processes, energy production is essential. Energy can be produced from different resources, but an important part of today’s energy resources are located offshore. Because of this reason, there are many critical infrastructures operating under offshore conditions such as oil and gas platforms, offshore renewable energy devices, offshore pipelines, etc. These structures are subjected to harsh marine environments, which can cause damage such as fatigue and corrosion. Therefore, this Special Issue will provide a compilation of numerical, experimental, and analytical studies related to “Structural Analysis and Failure Prevention in Offshore Engineering”, which covers a wide range of topics including, but not limited to, the following:

  • Structural analysis of offshore renewable energy devices, such as offshore wind, tidal energy, wave energy, and floating PV systems;
  • Structural analysis of offshore platforms;
  • Structural analysis of pipelines and subsea systems;
  • Risk and reliability-based approaches applied to offshore structures;
  • Structural health monitoring of offshore structures;
  • Corrosion;
  • Ice–structure interactions;
  • Collision mechanics;
  • Inspection and repair of offshore structures;
  • Fatigue and fracture.

Prof. Dr. Selda Oterkus
Prof. Dr. Erkan Oterkus
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • offshore renewable energy devices
  • offshore platforms
  • subsea pipelines
  • marine structures
  • fracture mechanics
  • corrosion
  • structural health monitoring
  • ice-structure interactions

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Published Papers (5 papers)

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Research

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20 pages, 27858 KiB  
Article
An Optimized GWO-BPNN Model for Predicting Corrosion Fatigue Performance of Stay Cables in Coastal Environments
by Liping Zhou and Guowen Yao
J. Mar. Sci. Eng. 2024, 12(12), 2308; https://doi.org/10.3390/jmse12122308 - 15 Dec 2024
Viewed by 662
Abstract
Corrosion and fatigue damage of high-strength steel wires in cable-stayed bridges in coastal environments can seriously affect the reliability of bridges. Previous studies have focused on isolated factors such as corrosion rates or stress ratios, failing to capture the complex interactions between multiple [...] Read more.
Corrosion and fatigue damage of high-strength steel wires in cable-stayed bridges in coastal environments can seriously affect the reliability of bridges. Previous studies have focused on isolated factors such as corrosion rates or stress ratios, failing to capture the complex interactions between multiple variables. In response to the critical need for accurate fatigue life prediction of high-strength steel wires under corrosive conditions, this study proposes an innovative prediction model that combines Grey Wolf Optimization (GWO) with a Backpropagation Neural Network (BPNN). The optimized GWO-BPNN model significantly enhances prediction accuracy, stability, generalization, and convergence speed. By leveraging GWO for efficient hyperparameter optimization, the model effectively reduces overfitting and strengthens robustness under varying conditions. The test results demonstrate the model’s high performance, achieving an R2 value of 0.95 and an RMSE of 140.45 on the test set, underscoring its predictive reliability and practical applicability. The GWO-BPNN model excels in capturing complex, non-linear dependencies within fatigue data, outperforming conventional prediction methods. Sensitivity analysis identifies stress range, average stress, and mass loss as primary determinants of fatigue life, highlighting the dominant influence of corrosion and stress factors on structural degradation. These results confirm the model’s interpretability and practical utility in pinpointing key factors that impact fatigue life. Overall, this study establishes the GWO-BPNN model as a highly accurate and adaptable tool, offering substantial support for advancing predictive maintenance strategies and enhancing material resilience in corrosive environments. Full article
(This article belongs to the Special Issue Structural Analysis and Failure Prevention in Offshore Engineering)
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20 pages, 6396 KiB  
Article
Underwater Line Monitoring Using Optimally Placed Inclinometers
by Chungkuk Jin and Seong Hyeon Hong
J. Mar. Sci. Eng. 2024, 12(11), 1939; https://doi.org/10.3390/jmse12111939 - 29 Oct 2024
Cited by 1 | Viewed by 605
Abstract
Underwater monitoring presents challenges related to maintaining a continuous power supply and communication, necessitating the use of a smaller number of sensors to effectively cover the entire line. An underwater line tracking method is proposed to evaluate global behaviors and stresses in real [...] Read more.
Underwater monitoring presents challenges related to maintaining a continuous power supply and communication, necessitating the use of a smaller number of sensors to effectively cover the entire line. An underwater line tracking method is proposed to evaluate global behaviors and stresses in real time. The method employs angles at several points on the line, as well as displacements and curvatures at both ends. In this method, any line displacement, angle, and curvature are expressed as Fourier series, and Fourier coefficients are obtained by utilizing sensor data. Then, the behavior of any line location is assessed. In addition, to reduce the number of sensors and improve accuracy, optimal inclinometer locations are determined by a genetic algorithm. The proposed line tracking algorithm was validated through two numerical examples; one with an inclined tunnel and one with a marine steel catenary riser attached to a Floating Production Storage and Offloading (FPSO) vessel. Through these examples, the proposed algorithm was proven to capture global behaviors accurately when optimally located sensors are used. In the riser monitoring case, the optimized sensor placement with eight intermediate sensors achieved an average mean distance error of 1.91 m, which is lower than the 2.65 m error obtained with ten intermediate sensors without optimization. Full article
(This article belongs to the Special Issue Structural Analysis and Failure Prevention in Offshore Engineering)
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30 pages, 10186 KiB  
Article
An Improved Convolutional Neural Network for Pipe Leakage Identification Based on Acoustic Emission
by Weidong Xu, Jiwei Huang, Lianghui Sun, Yixin Yao, Fan Zhu, Yaoguo Xie and Meng Zhang
J. Mar. Sci. Eng. 2024, 12(10), 1720; https://doi.org/10.3390/jmse12101720 - 30 Sep 2024
Cited by 1 | Viewed by 1645
Abstract
Oil and gas pipelines are the lifelines of the energy market, but due to long-term use and environmental factors, these pipelines are prone to corrosion and leaks. Offshore oil and gas pipeline leaks, in particular, can lead to severe consequences such as platform [...] Read more.
Oil and gas pipelines are the lifelines of the energy market, but due to long-term use and environmental factors, these pipelines are prone to corrosion and leaks. Offshore oil and gas pipeline leaks, in particular, can lead to severe consequences such as platform fires and explosions. Therefore, it is crucial to accurately and swiftly identify oil and gas leaks on offshore platforms. This is of significant importance for improving early warning systems, enhancing maintenance efficiency, and reducing economic losses. Currently, the efficiency of identifying leaks in offshore platform pipelines still needs improvement. To address this, the present study first established an experimental platform to simulate pipeline leaks in a marine environment. Laboratory leakage signal data were collected, and on-site noise data were gathered from the “Liwan 3-1” offshore oil and gas platform. By integrating leakage signals with on-site noise data, this study aimed to closely mimic real-world application scenarios. Subsequently, several neural network-based leakage identification methods were applied to the integrated dataset, including a probabilistic neural network (PNN) combined with time-domain feature extraction, a Backpropagation Neural Network (BPNN) optimized with simulated annealing and particle swarm optimization, and a Long Short-Term Memory Network (LSTM) combined with Mel-Frequency Cepstral Coefficients (MFCC). Corresponding models were constructed, and the effectiveness of leak detection was validated using test sets. Additionally, this paper proposes an improved convolutional neural network (CNN) leakage detection technology named SART-1DCNN. This technology optimizes the network architecture by introducing attention mechanisms, transformer modules, residual blocks, and combining them with Dropout and optimization algorithms, which significantly enhances data recognition accuracy. It achieves a high accuracy rate of 99.44% on the dataset. This work is capable of detecting pipeline leaks with high accuracy. Full article
(This article belongs to the Special Issue Structural Analysis and Failure Prevention in Offshore Engineering)
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35 pages, 13522 KiB  
Article
Life Assessment of Deep-Sea Observation Windows under Different Design Considerations
by Zhihao He, Fang Wang, Jinfei Zhang, Bingxiong Zhao, Yu Wu, Ruilong Luo and Fengluo Chen
J. Mar. Sci. Eng. 2024, 12(6), 1017; https://doi.org/10.3390/jmse12061017 - 18 Jun 2024
Viewed by 1246
Abstract
As a key component of deep-sea manned submersibles, the observation window is usually constructed with polymethyl methacrylate (PMMA) material. During the design of the observation windows, the consideration of actual lifespan and its influential factors is insufficient. There are no clear provisions in [...] Read more.
As a key component of deep-sea manned submersibles, the observation window is usually constructed with polymethyl methacrylate (PMMA) material. During the design of the observation windows, the consideration of actual lifespan and its influential factors is insufficient. There are no clear provisions in the widely applied specifications. In this paper, based on the continuum damage mechanics model, combined with the viscoelastic relationship of PMMA material, a series of calculations were performed on the PMMA observation window. The parametric analysis of the fatigue crack-initiation life of the observation window at various thickness-to-diameter ratios (1.6, 1.4, 1.2, and 1.0), different friction coefficients (0.1, 0.2, and 0.3), and different transition arc radii (4000 mm and 6000 mm) was carried out. The calculated crack positions in the numerical mode used for validation closely align with those in the tested window. And simulation results show that the fatigue life of the observation window gradually decreases with the decrease in the thickness–diameter ratio and the increase in the friction coefficient. However, the increase in the transition arc radius will prolong the fatigue life of the observation window, which is higher than that of the original structure. Full article
(This article belongs to the Special Issue Structural Analysis and Failure Prevention in Offshore Engineering)
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Review

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19 pages, 3456 KiB  
Review
Review of Implosion Design Considerations for Underwater Composite Pressure Vessels
by Helio Matos, Akongnwi Nfor Ngwa, Birendra Chaudhary and Arun Shukla
J. Mar. Sci. Eng. 2024, 12(9), 1468; https://doi.org/10.3390/jmse12091468 - 23 Aug 2024
Cited by 4 | Viewed by 1928
Abstract
The implosion of underwater composite structures is a critical and complex engineering problem, necessitating high-strength, lightweight, and corrosion-resistant materials for deep-sea applications. This manuscript reviews the intricate failure mechanisms of composite structures, focusing on cylindrical structures under extreme underwater conditions. The recent Titan [...] Read more.
The implosion of underwater composite structures is a critical and complex engineering problem, necessitating high-strength, lightweight, and corrosion-resistant materials for deep-sea applications. This manuscript reviews the intricate failure mechanisms of composite structures, focusing on cylindrical structures under extreme underwater conditions. The recent Titan submersible implosion serves as a case study, highlighting the significance of rigorous design considerations. Key topics include material degradation, buckling instability, and material failure, with a detailed analysis of composite layup optimization and manufacturing processes such as filament winding and roll wrapping. The manuscript underscores the need for comprehensive testing, advanced simulation techniques, and monitoring system integration to ensure the safety and effectiveness of composite pressure hulls. Future research should focus on developing more accurate failure models, optimizing manufacturing processes, and enhancing material properties through innovations in composite science to realize the full potential of composite materials in deep-sea applications. Full article
(This article belongs to the Special Issue Structural Analysis and Failure Prevention in Offshore Engineering)
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