An Inverse FEM for Structural Health Monitoring of a Containership: Sensor Network Optimization for Accurate Displacement, Strain, and Internal Force Reconstruction
In naval engineering, particular attention has been given to containerships, as these structures are constantly exposed to potential damage during service hours and since they are essential for large-scale transportation. To assess the structural integrity of these ships and to ensure the safety of...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/276 |
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| author | Jacopo Bardiani Christian Oppezzo Andrea Manes Claudio Sbarufatti |
| author_facet | Jacopo Bardiani Christian Oppezzo Andrea Manes Claudio Sbarufatti |
| author_sort | Jacopo Bardiani |
| collection | DOAJ |
| description | In naval engineering, particular attention has been given to containerships, as these structures are constantly exposed to potential damage during service hours and since they are essential for large-scale transportation. To assess the structural integrity of these ships and to ensure the safety of the crew and the cargo being transported, it is essential to adopt structural health monitoring (SHM) strategies that enable real-time evaluations of a ship’s status. To achieve this, this paper introduces an advancement in the field of smart sensing and SHM that improves ship monitoring and diagnostic capabilities. This is accomplished by a framework that combines the inverse finite element method (iFEM) with the definition of an optimal Fiber Bragg Gratings-based sensor network for the reconstruction of the full field of displacement; strain; and finally, cross-section internal forces. The optimization of the sensor network was performed by defining a multi-objective function that simultaneously considers the accuracy of the displacement field reconstruction and the associated cost of the sensor network. The framework was successfully applied to a mid-portion of a containership case, demonstrating its effective applicability in real and complex scenarios. |
| format | Article |
| id | doaj-art-76132b7a16c84a39a33d15ba5f615394 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-76132b7a16c84a39a33d15ba5f6153942025-01-10T13:21:26ZengMDPI AGSensors1424-82202025-01-0125127610.3390/s25010276An Inverse FEM for Structural Health Monitoring of a Containership: Sensor Network Optimization for Accurate Displacement, Strain, and Internal Force ReconstructionJacopo Bardiani0Christian Oppezzo1Andrea Manes2Claudio Sbarufatti3Department of Mechanical Engineering, Politecnico di Milano, Via G. La Masa 1, 20156 Milano, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, Via G. La Masa 1, 20156 Milano, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, Via G. La Masa 1, 20156 Milano, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, Via G. La Masa 1, 20156 Milano, ItalyIn naval engineering, particular attention has been given to containerships, as these structures are constantly exposed to potential damage during service hours and since they are essential for large-scale transportation. To assess the structural integrity of these ships and to ensure the safety of the crew and the cargo being transported, it is essential to adopt structural health monitoring (SHM) strategies that enable real-time evaluations of a ship’s status. To achieve this, this paper introduces an advancement in the field of smart sensing and SHM that improves ship monitoring and diagnostic capabilities. This is accomplished by a framework that combines the inverse finite element method (iFEM) with the definition of an optimal Fiber Bragg Gratings-based sensor network for the reconstruction of the full field of displacement; strain; and finally, cross-section internal forces. The optimization of the sensor network was performed by defining a multi-objective function that simultaneously considers the accuracy of the displacement field reconstruction and the associated cost of the sensor network. The framework was successfully applied to a mid-portion of a containership case, demonstrating its effective applicability in real and complex scenarios.https://www.mdpi.com/1424-8220/25/1/276inverse finite element methodinternal force reconstructioncontainershipsmulti-objective functionstructural health monitoring |
| spellingShingle | Jacopo Bardiani Christian Oppezzo Andrea Manes Claudio Sbarufatti An Inverse FEM for Structural Health Monitoring of a Containership: Sensor Network Optimization for Accurate Displacement, Strain, and Internal Force Reconstruction Sensors inverse finite element method internal force reconstruction containerships multi-objective function structural health monitoring |
| title | An Inverse FEM for Structural Health Monitoring of a Containership: Sensor Network Optimization for Accurate Displacement, Strain, and Internal Force Reconstruction |
| title_full | An Inverse FEM for Structural Health Monitoring of a Containership: Sensor Network Optimization for Accurate Displacement, Strain, and Internal Force Reconstruction |
| title_fullStr | An Inverse FEM for Structural Health Monitoring of a Containership: Sensor Network Optimization for Accurate Displacement, Strain, and Internal Force Reconstruction |
| title_full_unstemmed | An Inverse FEM for Structural Health Monitoring of a Containership: Sensor Network Optimization for Accurate Displacement, Strain, and Internal Force Reconstruction |
| title_short | An Inverse FEM for Structural Health Monitoring of a Containership: Sensor Network Optimization for Accurate Displacement, Strain, and Internal Force Reconstruction |
| title_sort | inverse fem for structural health monitoring of a containership sensor network optimization for accurate displacement strain and internal force reconstruction |
| topic | inverse finite element method internal force reconstruction containerships multi-objective function structural health monitoring |
| url | https://www.mdpi.com/1424-8220/25/1/276 |
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