Machine Learning and Optimization for Marine Structure

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: closed (30 April 2023) | Viewed by 8677

Special Issue Editors


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Guest Editor
Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico-Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal
Interests: fatigue and fracture; ultimate strength; marine structures; structural reliability; risk-based inspection; unsupervised machine learning; deep neural network; signal processing; lifecycle assessment

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Guest Editor
Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
Interests: marine structural design & analysis; fatigue and fracture mechanics; structural degradation; ultimate limit dtate analysis; structural reliability; risk-based maintenance; offshore wind farm
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Special Issue Information

Dear Colleagues,

Until very recently, physics-based analytical and numerical simulations were used heavily to identify optimal design solutions and effective inspection and maintenance planning for marine structures, ensuring sufficient safety levels at minimal cost. With the introduction of advanced machine learning techniques, nature-inspired efficient optimization algorithms and high-performance computing, there is a paradigm shift toward big-data-driven models with the aim of achieving more reliable and cost-effective solutions. The importance of machine learning and optimization is only expected to grow as big data collected by structural health monitoring systems are incorporated into the real-time asset reliability management of marine structures via digital twins and intelligent decision support systems.

In the light of these developments, we are delighted to announce a Special Issue entitled “Machine Learning and Optimization for Marine Structure”, which will collect original and impactful papers presenting theoretical, analytical, empirical, and numerical studies of engineering applications regarding machine learning and optimization for marine structures. Review papers which provide a critical view on the latest developments in terms of methods, modeling, and analysis are also encouraged.

This Special Issue covers a wide range of topics regarding machine learning and optimization, offering safe and cost-effective solutions for marine structures. Particular attention will be given to the application of supervised, unsupervised, and reinforcement machine learning applied to the structural safety problem, tackling failure mechanisms with catastrophic consequences. Furthermore, this Special Issue invites novel research on deterministic and stochastic optimization techniques dealing with design, structural integrity management, risk-based inspection and maintenance, the lifecycle performance of ships, oil and gas platforms, and offshore renewable energy devices. Studies that focus on the development of data-driven decision-support systems, intelligent maintenance systems, and digital twins for the safety of marine structures are also welcome.

Topics relevant to the Special Issue on “Machine Learning and Optimization for Marine Structure” include (but are not limited to):

  • Fatigue and fracture;
  • Ultimate strength;
  • Corrosion degradation;
  • Supervised and unsupervised machine learning;
  • Reinforcement learning;
  • Deep learning;
  • Damage detection;
  • Structural health monitoring;
  • Reliability-based optimization;
  • Risk-based design, inspection and maintenance.

Dr. Baran Yeter
Prof. Dr. Yordan Garbatov
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

  • supervised machine learning
  • unsupervised machine learning
  • reinforcement learning
  • damage detection
  • structural health monitoring
  • reliability-based design optimisation
  • nature-inspired optimisation algorithms
  • risk-based inspection and maintenance
  • fatigue & fracture
  • ultimate strength
  • corrosion

Published Papers (4 papers)

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Research

16 pages, 3886 KiB  
Article
Improved Ship Object Detection in Low-Illumination Environments Using RetinaMFANet
by Ruixin Ma, Kexin Bao and Yong Yin
J. Mar. Sci. Eng. 2022, 10(12), 1996; https://doi.org/10.3390/jmse10121996 - 15 Dec 2022
Cited by 5 | Viewed by 2589
Abstract
Video-based ship object detection has long been a popular research issue that has received attention in the water transportation industry. However, in low-illumination environments, such as at night or in fog, the water environment has a complex variety of light sources, video surveillance [...] Read more.
Video-based ship object detection has long been a popular research issue that has received attention in the water transportation industry. However, in low-illumination environments, such as at night or in fog, the water environment has a complex variety of light sources, video surveillance images are often accompanied by noise, and information on the details of objects in images is worsened. These problems cause high rates of false detection and missed detection when performing object detection for ships in low-illumination environments. Thus, this paper takes the detection of ship objects in low-illumination environments at night as the research object. The technical difficulties faced by object detection algorithms in low-illumination environments are analyzed, and a dataset of ship images is constructed by collecting images of ships (in the Nanjing section of Yangtze River in China) in low-illumination environments. In view of the outstanding performance of the RetinaNet model in general object detection, a new multiscale feature fusion network structure for a feature extraction module is proposed based on the same network architecture, in such a way that the extraction of more potential feature information from low-illumination images can be realized. In line with the feature detection network, the regression and classification detection network for anchor boxes is improved by means of the attention mechanism, guiding the network structure in the detection of object features. Moreover, the design and optimization of the augmentation of multiple random images and prior bounding boxes in the training process are also carried out. Finally, on the basis of experimental validation analysis, the optimized detection model was able to improve ship detection accuracy by 3.7% with a limited decrease in FPS (frames per second), and has better results in application. Full article
(This article belongs to the Special Issue Machine Learning and Optimization for Marine Structure)
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19 pages, 4684 KiB  
Article
The Effect of Data Skewness on the LSTM-Based Mooring Load Prediction Model
by Hangyu Chen, Yinglei Bu, Kun Zong, Limin Huang and Wei Hao
J. Mar. Sci. Eng. 2022, 10(12), 1931; https://doi.org/10.3390/jmse10121931 - 7 Dec 2022
Cited by 6 | Viewed by 1639
Abstract
The working condition of the floating platform will be affected by wind and waves in the marine environment. Therefore, it is of great importance to carry out real-time prediction research on the mooring load for ensuring the normal operation of the floating platform. [...] Read more.
The working condition of the floating platform will be affected by wind and waves in the marine environment. Therefore, it is of great importance to carry out real-time prediction research on the mooring load for ensuring the normal operation of the floating platform. Current researches have focused on the real-time prediction of mooring load using the machine learning method, but most of the studies are about the application and generalization analysis of different models. There are few studies on the influence of data distribution characteristics on prediction accuracy. In view of the above problems, this paper investigates the effect of data skewness on the prediction performance for the deep learning model. The long short-term memory (LSTM) neural network is applied to construct the mooring load prediction model. The numerical simulation datasets of the deep water semi-submersible platform are employed in model training and data analysis. The prediction performance of the model is preliminarily verified based on the simulation results. Meanwhile, the distribution characteristics of mooring load data under different sea states are analyzed and a skewness processing method based on the Box-Cox Transformation (BCT) is proposed. The effect of data skewness on prediction accuracy is further investigated. The comparison results indicate that reducing the mooring load data skewness can effectively improve the prediction accuracy of LSTM model. Full article
(This article belongs to the Special Issue Machine Learning and Optimization for Marine Structure)
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14 pages, 9931 KiB  
Article
A Framework for Optimal Sensor Placement to Support Structural Health Monitoring
by Shen Li, Andrea Coraddu and Feargal Brennan
J. Mar. Sci. Eng. 2022, 10(12), 1819; https://doi.org/10.3390/jmse10121819 - 25 Nov 2022
Cited by 6 | Viewed by 1372
Abstract
Offshore or drydock inspection performed by trained surveyors is required within the integrity management of an in-service marine structure to ensure safety and fitness for purpose. However, these physical inspection activities can lead to a considerable increase in lifecycle cost and significant downtime, [...] Read more.
Offshore or drydock inspection performed by trained surveyors is required within the integrity management of an in-service marine structure to ensure safety and fitness for purpose. However, these physical inspection activities can lead to a considerable increase in lifecycle cost and significant downtime, and they can impose hazards for the surveyors. To this end, the use of a structural health monitoring (SHM) system could be an effective resolution. One of the key performance indicators of an SHM system is its ability to predict the structural response of unmonitored locations by using monitored data, i.e., an inverse prediction problem. This is highly relevant in practical engineering, since monitoring can only be performed at limited and discrete locations, and it is likely that structurally critical areas are inaccessible for the installation of sensors. An accurate inverse prediction can be achieved, ideally, via a dense sensor network such that more data can be provided. However, this is usually economically unfeasible due to budget limits. Hence, to improve the monitoring performance of an SHM system, an optimal sensor placement should be developed. This paper introduces a framework for optimising the sensor placement scheme to support SHM. The framework is demonstrated with an illustrative example to optimise the sensor placement of a cantilever steel plate. The inverse prediction problem is addressed by using a radial basis function approach, and the optimisation is carried out by means of an evolutionary algorithm. The results obtained from the demonstration support the proposal. Full article
(This article belongs to the Special Issue Machine Learning and Optimization for Marine Structure)
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16 pages, 18003 KiB  
Article
Neural Network-Based Underwater Object Detection off the Coast of the Korean Peninsula
by Won-Ki Kim, Ho Seuk Bae, Su-Uk Son and Joung-Soo Park
J. Mar. Sci. Eng. 2022, 10(10), 1436; https://doi.org/10.3390/jmse10101436 - 5 Oct 2022
Cited by 2 | Viewed by 1274
Abstract
Recently, neural network-based deep learning techniques have been actively applied to detect underwater objects in sonar (sound navigation and ranging) images. However, unlike optical images, acquiring sonar images is extremely time- and cost-intensive, and therefore securing sonar data and conducting related research can [...] Read more.
Recently, neural network-based deep learning techniques have been actively applied to detect underwater objects in sonar (sound navigation and ranging) images. However, unlike optical images, acquiring sonar images is extremely time- and cost-intensive, and therefore securing sonar data and conducting related research can be rather challenging. Here, a side-scan sonar was used to obtain sonar images to detect underwater objects off the coast of the Korean Peninsula. For the detection experiments, we used an underwater mock-up model with a similar size, shape, material, and acoustic characteristics to the target object that we wished to detect. We acquired various side-scan sonar images of the mock-up object against the background of mud, sand, and rock to account for the different characteristics of the coastal and seafloor environments of the Korean Peninsula. To construct a detection network suitable for the obtained sonar images from the experiment, the performance of five types of feature extraction networks and two types of optimizers was analyzed. From the analysis results, it was confirmed that performance was achieved when DarkNet-19 was used as the feature extraction network, and ADAM was applied as the optimizer. However, it is possible that there are feature extraction network and optimizer that are more suitable for our sonar images. Therefore, further research is needed. In addition, it is expected that the performance of the modified detection network can be more improved if additional images are obtained. Full article
(This article belongs to the Special Issue Machine Learning and Optimization for Marine Structure)
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