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AI-Enhanced Dynamics and Reliability Analysis of Marine Structures
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AI-Enhanced Dynamics and Reliability Analysis of Marine 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: 5 August 2026 | Viewed by 2032

Editors

Dr. Qingji Zhou

E-Mail Website
Guest Editor
School of Marine Science and Technology, Tianjin University, Tianjin 300072, China
Interests: maritime transport and safety; sustainability; shipping management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zunfeng Du

E-Mail Website
Guest Editor
School of Civil Engineering, Tianjin University, Tianjin 300072, China
Interests: intelligent AI-Based design and operation offshore structures; dynamic response and safety assessment of marine structure; offshore structure reliability analysis; marine renewable energy; structural health monitoring
Dr. Yan Li

E-Mail Website
Guest Editor
School of Civil Engineering, Tianjin University, Tianjin 300072, China
Interests: nonlinear dynamics of offshore floating structures; marine renewable energy; floating offshore wind turbine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Marine structures operate in complex and highly uncertain ocean environments, where dynamic loads, environmental variability, and long-term degradation pose significant challenges to structural safety and reliability. This Special Issue focuses on recent advances in the dynamics and reliability analysis of marine structures, emphasizing innovative theoretical, numerical, and experimental approaches. Particular attention is given to the integration of artificial intelligence, data-driven methods, and physics-informed modeling with traditional structural dynamics and reliability theories to improve response prediction, uncertainty quantification, and risk assessment. Topics of interest include dynamic response analysis of offshore and marine structures under waves, currents, wind, and coupled environmental loads; vibration and fatigue analysis; reliability and probabilistic assessment methods; structural health monitoring and damage detection; digital twin and intelligent monitoring frameworks; AI-assisted modeling, prediction, and decision-making; and lifecycle safety and reliability management of marine structures. The Special Issue aims to provide a comprehensive platform for interdisciplinary research that advances the understanding, assessment, and management of marine structural performance in complex and extreme environments.

Dr. Qingji Zhou
Prof. Dr. Zunfeng Du
Dr. Yan Li
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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 semimonthly 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

  • marine structures
  • structural dynamics
  • reliability analysis
  • artificial intelligence
  • data-driven modelling
  • structural health monitoring

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Published Papers (3 papers)

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Research

24 pages, 9788 KB  
Article
Short-Term Motion Prediction of an FLNG System for Collision Risk Mitigation During Side-by-Side Offloading Operations
by Bin Song, Baoji Zhang, Kexu Zhong, Jiayang Sun and Yutao Cui
J. Mar. Sci. Eng. 2026, 14(13), 1206; https://doi.org/10.3390/jmse14131206 - 30 Jun 2026
Viewed by 186
Abstract
Floating liquefied natural gas (FLNG) facilities integrate natural gas liquefaction, storage, and offloading into a single vessel. During ship-to-ship (STS) side-by-side offloading, an LNG carrier (LNGC) moors alongside the FLNG to transfer liquefied cargo through a loading-arm system. The hydrodynamic interactions between the [...] Read more.
Floating liquefied natural gas (FLNG) facilities integrate natural gas liquefaction, storage, and offloading into a single vessel. During ship-to-ship (STS) side-by-side offloading, an LNG carrier (LNGC) moors alongside the FLNG to transfer liquefied cargo through a loading-arm system. The hydrodynamic interactions between the two vessels, combined with environmental loads, can lead to excessive relative motions that pose a risk of collision or damage to the loading arms and fenders. Accurate short-term prediction of vessel motions would provide operators with advance warning of potentially dangerous conditions, allowing preventive actions to be taken. This study presents a data-driven approach to short-term motion prediction using experimental data obtained from comprehensive basin model tests of an FLNG system. The model tests covered 15 environmental conditions, including survival conditions (100-year return period) and operating conditions (1-year return period), under both single-vessel and side-by-side configurations. Three prediction methods were evaluated: an autoregressive linear model, a single-degree-of-freedom multi-layer perceptron, and a multi-head attention cross-coupling network (MAC-Net) that leverages temporal attention, cross-DOF graph message passing, and multi-task learning with uncertainty-weighted loss. The results show that surge, sway, and yaw can be predicted with high skill scores at model-scale horizons of up to 4 s (32 s full-scale equivalent), while heave and pitch exhibit limited predictability beyond 2 s model scale. The MAC-Net model demonstrates particular advantages for roll prediction, achieving a skill score of 0.88 at a 4 s model-scale horizon compared to 0.76 for the conventional method, attributable to the physical coupling between roll and the horizontal-plane motions through the mooring system. These findings support a practical early warning concept in which horizontal-plane motions provide advance collision alerts and heave/pitch are treated as short-horizon monitoring quantities. Full article
(This article belongs to the Special Issue AI-Enhanced Dynamics and Reliability Analysis of Marine Structures)
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27 pages, 5739 KB  
Article
Baseline-Conditioned Spatial Heterogeneity in Ensemble-Learning Correction for Global Hourly Sea-Level Reconstruction
by Yu Hao, Yixuan Tang, Wen Du, Yang Li and Min Xu
J. Mar. Sci. Eng. 2026, 14(8), 697; https://doi.org/10.3390/jmse14080697 - 8 Apr 2026
Viewed by 677
Abstract
This study examines how assessments of coastal extreme sea levels depend on the separability and reconstructability of the astronomical tide in hourly sea-level records. Using a global tide-gauge network, it proposes an ensemble-learning correction framework that integrates a physical-baseline threshold with multi-criteria consistency [...] Read more.
This study examines how assessments of coastal extreme sea levels depend on the separability and reconstructability of the astronomical tide in hourly sea-level records. Using a global tide-gauge network, it proposes an ensemble-learning correction framework that integrates a physical-baseline threshold with multi-criteria consistency testing to determine whether machine-learning enhancement is genuinely effective across stations and time windows. The analysis uses hourly records from 528 UHSLC tide gauges, with 31-day short sequences used to reconstruct 180-day sea-level variability. Taking the physical tidal model as the baseline, residuals are corrected using Extremely Randomized Trees, Random Forest, and Gradient Boosting. To avoid false improvement driven solely by error reduction, a hierarchical decision framework is established. Baseline model quality is first screened using NSE and the coefficient of determination, after which mathematical artefacts are identified through diagnostics of peak suppression and variance shrinkage. A five-level classification is then derived from the convergent evidence of twelve performance metrics and four statistical significance tests. The results show a consistent global pattern across all three algorithms. Approximately 57% of stations meet the criterion for genuine improvement, whereas about 42% are associated with an unreliable physical baseline, indicating that the dominant source of failure arises not from the ensemble-learning algorithms themselves, but from spatially varying limitations in the underlying physical baseline. Spatially, the credibility of machine-learning correction is strongly conditioned by baseline quality: stations with effective correction are more continuous along the eastern North Atlantic and European coasts, whereas stations with ineffective correction are more concentrated in the Gulf of Mexico, the Caribbean, and the marginal seas and archipelagic regions of the western Pacific. These results indicate that the observed spatial heterogeneity primarily reflects geographically varying physical and dynamical conditions that control baseline reliability and residual learnability, rather than a standalone difference in the intrinsic capability of ensemble learning itself. Full article
(This article belongs to the Special Issue AI-Enhanced Dynamics and Reliability Analysis of Marine Structures)
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14 pages, 1566 KB  
Article
Field-Level Uncertainty Quantification for AI-Based Ship Hull Surface Pressure Prediction
by Jeongbeom Seo and Inwon Lee
J. Mar. Sci. Eng. 2026, 14(5), 504; https://doi.org/10.3390/jmse14050504 - 6 Mar 2026
Viewed by 765
Abstract
This study investigates uncertainty quantification for field-level ship hull surface pressure predictions using a U-Net-based data-driven model. A speed-conditioned U-Net is trained on a large CFD dataset covering multiple ship types and velocity conditions to predict pressure distributions on hull surfaces. The model [...] Read more.
This study investigates uncertainty quantification for field-level ship hull surface pressure predictions using a U-Net-based data-driven model. A speed-conditioned U-Net is trained on a large CFD dataset covering multiple ship types and velocity conditions to predict pressure distributions on hull surfaces. The model outputs the mean pressure and log-variance at each grid location using a negative log-likelihood loss, allowing aleatoric uncertainty to be estimated, while epistemic uncertainty is quantified by a deep ensemble of independently trained models. The reliability and calibration of the predicted confidence intervals are evaluated at the field level. The results show that calibration stabilizes as ensemble size increases, and coverage slightly exceeds nominal confidence levels. Uncertainty decomposition indicates that aleatoric uncertainty dominates and is insensitive to ensemble size, while epistemic uncertainty primarily affects calibration. Elevated uncertainty is consistently observed near free-surface regions around the bow and stern, reflecting increased prediction difficulty. These findings demonstrate the effectiveness of deep-ensemble-based uncertainty quantification for CFD-driven pressure field prediction models. Full article
(This article belongs to the Special Issue AI-Enhanced Dynamics and Reliability Analysis of Marine Structures)
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