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JMSE
Special Issues
Monitoring and Evaluation of Marine Engineering Equipment and Structures
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Monitoring and Evaluation of Marine Engineering Equipment and Structures

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

Deadline for manuscript submissions: 1 June 2025 | Viewed by 5918

Special Issue Editors

Dr. Ziguang Jia

E-Mail Website
Guest Editor
School of Ocean Science and Technology, Dalian University of Technology, Panjin 124221, China
Interests: marine structure monitoring and assessment; fiber optic sensing technology; pipeline integrity management
Special Issues, Collections and Topics in MDPI journals
Dr. Peng Zhang

E-Mail Website
Guest Editor
College of Transportation Engineering, Dalian Maritime University, Dalian, China
Interests: marine engineering structures; structural health monitoring; structural vibration control
Prof. Dr. Yi Huang

E-Mail Website
Guest Editor
School of Naval Architecture and Ocean Engineering, Dalian University of Technology, Dalian 116024, China
Interests: structural analysis and environmental intensity of ships and marine structures corrosion protection for ships and marine engineering; damage evolution mechanism and life cycle safety management of offshore platform structures

Special Issue Information

Dear Colleagues,

Over the past few decades, structural health monitoring has seen significant development, benefiting from advancements in various technologies. Initially focused on onshore structures such as bridges and buildings, while SHM methods have been widely developed and progressively refined, their application to marine structures and equipment has been comparatively limited. Marine engineering structures, such as offshore oil rigs, wind turbines, and pipelines, play a crucial role in global energy production and transportation. Operating in harsh marine environments, these structures are susceptible to damage and degradation due to factors like corrosion and dynamic loads. Ensuring their structural integrity is paramount to prevent catastrophic failures, environmental disasters, and economic losses. In this context, intelligent monitoring and evaluation technologies have emerged. These technologies, through real-time monitoring, data acquisition, and analysis of marine engineering equipment and structures, can promptly identify potential risks and take corresponding measures to ensure the safe operation of marine engineering. To better understand these advancements and their applications, this Special Issue will showcase the latest research findings in relevant fields, providing a platform for rapid peer review and publication, and freely disseminating these research results for research, teaching, and reference purposes. High-quality papers discussing the following topics are particularly encouraged, and scientific analyses of practical cases are welcomed. Authors are encouraged to emphasize the importance, benefits, and significance of monitoring and evaluation for practical engineering applications. This Special Issue is dedicated to showcasing the latest developments in the monitoring, analysis, and maintenance of marine engineering structures. Through the integration of sensing technologies, artificial intelligence, and big data analytics, it aims to address current challenges and promote technological innovation and development in this critical field.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Monitoring, Management, Maintenance, and Safety Assessment System for Offshore Oil and Gas Platforms;
  • Stability and Dynamics Analysis;
  • Fatigue and Fracture Mechanics Analysis;
  • Advanced Monitoring Technologies and Methodology;
  • Corrosion Monitoring and Protection in Marine Environments;
  • Vibration Monitoring and Control Techniques;
  • Dynamic Response Analysis and Evaluation;
  • Modal Identification and Parameter Estimation;
  • Optimization of Sensor Deployment;
  • Analysis Methods Based on Big Data and Artificial Intelligence;
  • Structural Damage Detection and Evaluation;
  • Assessment of Ice Loads and Marine Biofouling Effects;
  • Lifetime Prediction and Extension Techniques for Structures;
  • Application of Novel Materials in Marine Engineering;
  • Real-Time Monitoring and Warning Systems for Marine Engineering Structures.

We look forward to receiving your contributions.

Dr. Ziguang Jia
Dr. Peng Zhang
Prof. Dr. Yi Huang
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

  • artificial intelligence
  • digital twin
  • structural maintenance
  • modal identification
  • structural dynamic analysis
  • model correction
  • load and response identification
  • life extension evaluation
  • fatigue analysis and fracture mechanics
  • smart materials and structures

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

23 pages, 14073 KiB  
Article
Reconstruction of Multi-Load Strain Field of Ship Stiffened Plate Based on iFEM and Analysis of Influencing Factors of Reconstruction Accuracy
by Guocai Chen, Xueliang Wang, Quanhua Zhu, Huawei Yang, Zhentao Jiang, Hao Xu, Mengdan Sun, Wei Chen, Haozheng Chen, Tao Zhang and Zheng Zhang
J. Mar. Sci. Eng. 2025, 13(2), 350; https://doi.org/10.3390/jmse13020350 - 14 Feb 2025
Viewed by 428
Abstract
This study utilizes the inverse finite element method (iFEM) to investigate the strain field reconstruction of ship stiffened plates under multiple loading conditions. The aim is to enhance the monitoring, safety, and reliability of ship structures through multi-condition strain field reconstruction. By applying [...] Read more.
This study utilizes the inverse finite element method (iFEM) to investigate the strain field reconstruction of ship stiffened plates under multiple loading conditions. The aim is to enhance the monitoring, safety, and reliability of ship structures through multi-condition strain field reconstruction. By applying iFEM, this research addresses the challenge of reconstructing strain fields from discrete strain measurements using a least-squares variational equation derived from elastic mechanics principles. The performance of iFEM was evaluated under five loading conditions: axial compression, non-uniform loading, torsion, combined axial compression with non-uniform loading, and combined axial compression with symmetric uniform loading. To mitigate boundary effects, an extended stiffened plate design was implemented. The results show significant improvements in reconstruction accuracy: under two specific loading conditions, the precision improved by 38.82% and 11.25%, respectively, compared to the original plate. This study underscores the potential of iFEM in improving the monitoring and safety of marine structures. Future work could explore the applicability of iFEM to other marine structures and scenarios, ensuring broader practical applications. Full article
(This article belongs to the Special Issue Monitoring and Evaluation of Marine Engineering Equipment and Structures)
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21 pages, 9294 KiB  
Article
Research on Ellipse-Based Transient Impact Source Localization Methodology for Ship Cabin Structure
by Xiufeng Huang, Rongwu Xu, Wenjing Yu, Xuan Ming and Shiji Wu
J. Mar. Sci. Eng. 2025, 13(2), 333; https://doi.org/10.3390/jmse13020333 - 12 Feb 2025
Viewed by 588
Abstract
This study explores the application of three localization methods in identifying transient impact sources in the ship cabin structure. These methods examined are based on energy curvature and cumulative error, time-reversed virtual focusing triangulation, and energy correlation localization. It presents an elliptical region-based [...] Read more.
This study explores the application of three localization methods in identifying transient impact sources in the ship cabin structure. These methods examined are based on energy curvature and cumulative error, time-reversed virtual focusing triangulation, and energy correlation localization. It presents an elliptical region-based transient impact source localization technique for the ship cabin structure. The center of the elliptical region is determined by calculating the arithmetic mean of the position coordinates obtained from three methods, and the long and short semi-axes of the ellipse are defined as three times the standard deviations in the horizontal and vertical directions, respectively, to construct an elliptical localization area for precise positioning. Experimental results indicate that the average error distance of this impact localization technique is 0.10 m, with the predicted position error of 22 impact points being 0 m. Among 15 impact points, 14 impact points have error distances ranging from 0 m to 0.40 m, while 1 impact point has an error distance of 1.08 m, primarily due to the weak connection between sensors and the ship cabin structure. The overall localization error of the ship cabin structure is low, meeting the required localization accuracy. Full article
(This article belongs to the Special Issue Monitoring and Evaluation of Marine Engineering Equipment and Structures)
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28 pages, 8539 KiB  
Article
Enhancing YOLOv5 Performance for Small-Scale Corrosion Detection in Coastal Environments Using IoU-Based Loss Functions
by Qifeng Yu, Yudong Han, Yi Han, Xinjia Gao and Lingyu Zheng
J. Mar. Sci. Eng. 2024, 12(12), 2295; https://doi.org/10.3390/jmse12122295 - 13 Dec 2024
Cited by 1 | Viewed by 1252
Abstract
The high salinity, humidity, and oxygen-rich environments of coastal marine areas pose serious corrosion risks to metal structures, particularly in equipment such as ships, offshore platforms, and port facilities. With the development of artificial intelligence technologies, image recognition-based intelligent detection methods have provided [...] Read more.
The high salinity, humidity, and oxygen-rich environments of coastal marine areas pose serious corrosion risks to metal structures, particularly in equipment such as ships, offshore platforms, and port facilities. With the development of artificial intelligence technologies, image recognition-based intelligent detection methods have provided effective support for corrosion monitoring in marine engineering structures. This study aims to explore the performance improvements of different modified YOLOv5 models in small-object corrosion detection tasks, focusing on five IoU-based improved loss functions and their optimization effects on the YOLOv5 model. First, the study utilizes corrosion testing data from the Zhoushan seawater station of the China National Materials Corrosion and Protection Science Data Center to construct a corrosion image dataset containing 1266 labeled images. Then, based on the improved IoU loss functions, five YOLOv5 models were constructed: YOLOv5-NWD, YOLOv5-Shape-IoU, YOLOv5-WIoU, YOLOv5-Focal-EIoU, and YOLOv5-SIoU. These models, along with the traditional YOLOv5 model, were trained using the dataset, and their performance was evaluated using metrics such as precision, recall, F1 score, and FPS. The results showed that YOLOv5-NWD performed the best across all metrics, with a 7.2% increase in precision and a 2.2% increase in F1 score. The YOLOv5-Shape-IoU model followed, with improvements of 4.5% in precision and 2.6% in F1 score. In contrast, the performance improvements of YOLOv5-Focal-EIoU, YOLOv5-SIoU, and YOLOv5-WIoU were more limited. Further analysis revealed that different IoU ratios significantly affected the performance of the YOLOv5-NWD model. Experiments showed that the 4:6 ratio yielded the highest precision, while the 6:4 ratio performed the best in terms of recall, F1 score, and confusion matrix results. In addition, this study conducted an assessment using four datasets of different sizes: 300, 600, 900, and 1266 images. The results indicate that increasing the size of the training dataset enables the model to find a better balance between precision and recall, that is, a higher F1 score, while also effectively improving the model’s processing speed. Therefore, the choice of an appropriate IoU ratio should be based on specific application needs to optimize model performance. This study provides theoretical support for small-object corrosion detection tasks, advances the development of loss function design, and enhances the detection accuracy and reliability of YOLOv5 in practical applications. Full article
(This article belongs to the Special Issue Monitoring and Evaluation of Marine Engineering Equipment and Structures)
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20 pages, 9949 KiB  
Article
Development of AI-Based Multisensory System for Monitoring Quay Wall Events
by Junsik Shin, Junyoung Park, Jongbin Won, Jongwoong Park and Jiyoung Min
J. Mar. Sci. Eng. 2024, 12(11), 1902; https://doi.org/10.3390/jmse12111902 - 24 Oct 2024
Viewed by 1176
Abstract
Structural monitoring of quay walls, where various events occur due to unexpected high waves, vessels, and heavy equipment, is essential. However, real-time events cannot be constantly monitored by on-site personnel. To resolve the aforementioned issues, this study proposes an innovative AI-powered, cloud-based wireless [...] Read more.
Structural monitoring of quay walls, where various events occur due to unexpected high waves, vessels, and heavy equipment, is essential. However, real-time events cannot be constantly monitored by on-site personnel. To resolve the aforementioned issues, this study proposes an innovative AI-powered, cloud-based wireless sensor system that incorporates a high-sensitivity accelerometer with an ultra-low noise level of 0.003 mg, designed to monitor the low response amplitude of massive quay walls. The sensor can be activated by a scheduled trigger or a long-rangefinder. Vessel detection is performed utilizing the AI-based object detection method, Faster R-CNN, which employs ResNet as the backbone network. The detected anchor box’s position and dimensions are subsequently processed to confirm the presence of a berthing vessel. The collected data are then transmitted wirelessly to a proposed cloud server through LTE communication in real-time. The developed system was installed on a caisson-type quay wall in Korea, where acceleration, tilt, temperature, and camera image data were analyzed to assess its performance for real-time event monitoring. The results demonstrated that the safety of quay walls can be automatically managed by monitoring events during berthing and mooring with the proposed system. Full article
(This article belongs to the Special Issue Monitoring and Evaluation of Marine Engineering Equipment and Structures)
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20 pages, 4544 KiB  
Article
Risk Assessment of Polar Drillship Operations Based on Bayesian Networks
by Qi Wang, Zixin Wang, Hongen Li, Xiaoming Huang, Qianjin Yue, Xiufeng Yue and Yanlin Wang
J. Mar. Sci. Eng. 2024, 12(10), 1873; https://doi.org/10.3390/jmse12101873 - 18 Oct 2024
Viewed by 923
Abstract
In the extreme polar marine environment, safety risks pose a significant threat to drilling vessels. By conducting a safety risk assessment, potential hazards can be predicted and identified, thereby significantly reducing the frequency of accidents and promoting the sustained stability of economic activities. [...] Read more.
In the extreme polar marine environment, safety risks pose a significant threat to drilling vessels. By conducting a safety risk assessment, potential hazards can be predicted and identified, thereby significantly reducing the frequency of accidents and promoting the sustained stability of economic activities. This paper investigates a Bayesian-network-based risk assessment model for polar drilling operations. Grey relational analysis was employed to identify the main risk factors. The model is trained using 525 valid incident sample data and is combined with expert knowledge. The accuracy rate is above 88%. Additionally, corresponding decision-making recommendations are provided through sensitivity analysis. The three most sensitive elements to fire nodes are human error, other causes, and equipment damage, with sensitivity coefficients of 0.046, 0.042, and 0.022, respectively. In terms of deck/handrail collision nodes, the highly sensitive elements are related to lifting (totally more than 0.1). For the events that have already transpired, the probabilities of most related nodes are 0.73 and 0.74, both of which are above 0.5, thereby validating the accuracy of forward and backward reasoning. Risk assessments based on Bayesian networks can offer pertinent decision-making recommendations and preventive measures. Full article
(This article belongs to the Special Issue Monitoring and Evaluation of Marine Engineering Equipment and Structures)
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