Prediction of Member Forces of Steel Tubes on the Basis of a Sensor System with the Use of AI
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| Publicado en: | Sensors vol. 25, no. 3 (2025), p. 919 |
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| Autor Principal: | |
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| Publicado: |
MDPI AG
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| Acceso en liña: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3165918682 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1424-8220 | ||
| 024 | 7 | |a 10.3390/s25030919 |2 doi | |
| 035 | |a 3165918682 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231630 |2 nlm | ||
| 100 | 1 | |a Li, Haiyu | |
| 245 | 1 | |a Prediction of Member Forces of Steel Tubes on the Basis of a Sensor System with the Use of AI | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The rapid development of AI (artificial intelligence), sensor technology, high-speed Internet, and cloud computing has demonstrated the potential of data-driven approaches in structural health monitoring (SHM) within the field of structural engineering. Algorithms based on machine learning (ML) models are capable of discerning intricate structural behavioral patterns from real-time data gathered by sensors, thereby offering solutions to engineering quandaries in structural mechanics and SHM. This study presents an innovative approach based on AI and a fiber-reinforced polymer (FRP) double-helix sensor system for the prediction of forces acting on steel tube members in offshore wind turbine support systems; this enables structural health monitoring of the support system. The steel tube as the transitional member and the FRP double helix-sensor system were initially modeled in three dimensions using ABAQUS finite element software. Subsequently, the data obtained from the finite element analysis (FEA) were inputted into a fully connected neural network (FCNN) model, with the objective of establishing a nonlinear mapping relationship between the inputs (strain) and the outputs (reaction force). In the FCNN model, the impact of the number of input variables on the model’s predictive performance is examined through cross-comparison of different combinations and positions of the six sets of input variables. And based on an evaluation of engineering costs and the number of strain sensors, a series of potential combinations of variables are identified for further optimization. Furthermore, the potential variable combinations were optimized using a convolutional neural network (CNN) model, resulting in optimal input variable combinations that achieved the accuracy level of more input variable combinations with fewer sensors. This not only improves the prediction performance of the model but also effectively controls the engineering cost. The model performance was evaluated using several metrics, including R2, MSE, MAE, and SMAPE. The results demonstrated that the CNN model exhibited notable advantages in terms of fitting accuracy and computational efficiency when confronted with a limited data set. To provide further support for practical applications, an interactive graphical user interface (GUI)-based sensor-coupled mechanical prediction system for steel tubes was developed. This system enables engineers to predict the member forces of steel tubes in real time, thereby enhancing the efficiency and accuracy of SHM for offshore wind turbine support systems. | |
| 653 | |a Strain gauges | ||
| 653 | |a Turbines | ||
| 653 | |a Machine learning | ||
| 653 | |a Simulation | ||
| 653 | |a Software | ||
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Metal fatigue | ||
| 653 | |a Sensors | ||
| 653 | |a Civil engineering | ||
| 653 | |a Structural engineering | ||
| 653 | |a Algorithms | ||
| 653 | |a Offshore | ||
| 653 | |a Drilling & boring machinery | ||
| 653 | |a Computer aided engineering--CAE | ||
| 700 | 1 | |a Chung, Heungjin | |
| 773 | 0 | |t Sensors |g vol. 25, no. 3 (2025), p. 919 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3165918682/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3165918682/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3165918682/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |