The motion forecasting study of floating offshore wind turbine using self-attention long short-term memory method

Ocean Engineering, Год журнала: 2024, Номер 310, С. 118709 - 118709

Опубликована: Июль 14, 2024

Язык: Английский

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A Novel Methodology for Structural Health Monitoring of Buildings Subjected to Earthquakes

Опубликована: Янв. 1, 2025

Recent advancements in sensor technology and data processing algorithms have revolutionized Structural Health Monitoring (SHM), enabling real-time monitoring analysis of structural responses to dynamic loads. As a result, many buildings are permanently instrumented with sensors, typically accelerometers, continuously record vibrational over time, hence generating huge amounts data. However, the extraction meaningful insights from recorded assist engineers building managers assessing conditions would be challenge. systems can programmed ground motion-induced vibrations that surpass specific trigger threshold levels. Nonetheless, there challenges long-term damage detection including automated previously data, limited number available nonlinear under severe earthquakes, name but few. In this paper, new methodology based on adaptive time-series (TS) models for SHM subjected earthquakes is introduced overcome these challenges. Using proposed technique, large set establishing reliable baseline structure using even as few two accelerometers (one one ground) achievable. The efficiency method large-scale structures sensors verified 3-D Finite Element (FE) model 5-story reinforced concrete (RC) SAP2000 platform. simulation results demonstrated accurate identification potential provided clear indication progression severity induced increases.

Язык: Английский

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Application of Artificial Intelligence to Support Design and Analysis of Steel Structures

Metals, Год журнала: 2025, Номер 15(4), С. 408 - 408

Опубликована: Апрель 4, 2025

In steel structural engineering, artificial intelligence (AI) and machine learning (ML) are improving accuracy, efficiency, automation. This review explores AI-driven approaches, emphasizing how AI models improve predictive capabilities, optimize performance, reduce computational costs compared to traditional methods. Inverse Machine Learning (IML) is a major focus since it helps engineers minimize reliance on iterative trial-and-error by allowing them identify ideal material properties geometric configurations depending predefined performance targets. Unlike conventional ML that mostly forward predictions, IML data-driven design generation, enabling more adaptive engineering solutions. Furthermore, underlined Explainable Artificial Intelligence (XAI), which enhances model transparency, interpretability, trust of AI. The paper categorizes applications in construction based their impact automation, health monitoring, failure prediction evaluation throughout research from 1990 2025. challenges such as data limitations, generalization, reliability, the need for physics-informed while examining AI’s role bridging real-world applications. By integrating into this work supports adoption ML, IML, XAI analysis design, paving way reliable interpretable practices.

Язык: Английский

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Structural performance and design of assembled aluminum alloy-concrete-carbon steel double-skin tubular column

Ocean Engineering, Год журнала: 2025, Номер 319, С. 120229 - 120229

Опубликована: Янв. 6, 2025

Язык: Английский

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Fatigue crack identification in jacket-type offshore platforms using a parallel multi-scale convolutional neural network

Proceedings of the Institution of Mechanical Engineers Part M Journal of Engineering for the Maritime Environment, Год журнала: 2025, Номер unknown

Опубликована: Янв. 30, 2025

Vibration-based damage detection techniques are widely applied in the structural health monitoring of offshore platforms. This study employs a parallel multi-scale convolutional neural network (PMSCNN) to analyze acceleration response signals collected from platforms, achieving localization fatigue cracks. The effectiveness proposed method is validated through numerical simulations jacket-type platforms subjected random wave excitations different directions. focuses on identifying crack platform components, addressing both single and multiple scenarios involving small To assess robustness against noise, Gaussian white noise varying intensities was added signals. results demonstrate that approach effectively identifies locates cracks exhibiting strong resistance.

Язык: Английский

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Real-Time Mooring Tension Prediction for Semi-Submersible Aquaculture Platforms by an EEMD-TCN-SA Neural Network Approach

Journal of Marine Science and Engineering, Год журнала: 2025, Номер 13(2), С. 327 - 327

Опубликована: Фев. 11, 2025

Precise prediction of mooring tension is essential for the safety and operational efficiency semi-submersible aquaculture platforms. Traditional numerical methods struggle with real-time performance due to nonlinear dynamic characteristics environmental loads. This study proposes a novel neural network approach enhance forecasting line responses, combining Ensemble Empirical Mode Decomposition (EEMD), Temporal Convolutional Networks (TCNs), Self-Attention (SA) mechanism. The training dataset encompasses time-domain analysis results, including tensions, motion total structural forces. Firstly, Pearson Correlation Analysis (PCA) utilized assess linear relationships among hydrodynamic variables. Subsequently, EEMD applied decompose data, which then combined highly correlated variables form input dataset. Finally, TCN model trained predict time series, while an SA mechanism integrated weigh significance different moments within sequence, thereby further enhancing accuracy. results demonstrate that evaluation metrics EEMD-TCN-SA outperform those other models, effectively predicting platforms significantly reducing errors.

Язык: Английский

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Online fatigue life prediction method of jacket structures based on proper orthogonal decomposition and rainflow counting algorithm

Structures, Год журнала: 2025, Номер 74, С. 108517 - 108517

Опубликована: Фев. 23, 2025

Язык: Английский

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An Innovative Data-Driven Approach for Detection of Linear and Nonlinear Damage in Building Structures Using Signal Processing and BiLSTM-Based Deep Learning

Journal of structural design and construction practice., Год журнала: 2025, Номер 30(3)

Опубликована: Март 25, 2025

Язык: Английский

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A hybrid VMD-IGA-LSTM model for dynamic response prediction of jacket offshore platform

Ocean Engineering, Год журнала: 2025, Номер 328, С. 121110 - 121110

Опубликована: Апрель 4, 2025

Язык: Английский

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Fatigue life assessment of marine structures accounting for multiple pit corrosion interactions: Coupling effects and crack initiation detection

Ocean Engineering, Год журнала: 2025, Номер 333, С. 121581 - 121581

Опубликована: Май 18, 2025

Язык: Английский

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