Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios
This paper focuses on optimizing sensor placement in structures for load monitoring applications. Such applications rely on sensor data to track changes in the structure. Monitoring accuracy relies on proper sensor placement. The goal is to maximize load monitoring accuracy under multiple loading sc...
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| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/13/7463 |
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| author | Wacław Kuś Waldemar Mucha Iyasu Tafese Jiregna |
| author_facet | Wacław Kuś Waldemar Mucha Iyasu Tafese Jiregna |
| author_sort | Wacław Kuś |
| collection | DOAJ |
| description | This paper focuses on optimizing sensor placement in structures for load monitoring applications. Such applications rely on sensor data to track changes in the structure. Monitoring accuracy relies on proper sensor placement. The goal is to maximize load monitoring accuracy under multiple loading scenarios while the number of sensors is kept smaller than the number of load cases. Building on prior work in which machine learning models predicted loads using only sensor readings without information on load location, this study continues that approach. It demonstrates that prediction models perform better when sensor networks are optimized. The novelty lies in a newly formulated objective function, allowing for optimization of sensor number, positions, and orientations across multiple load cases and measurement types. The goal is to minimize the differences between maximal responses of the structure and detected responses by the sensors (for all possible load cases). The method is validated on a numerical model of a composite structure with 1–3 sensors under seven different load cases. Load predictions from sensors in optimized locations are compared to predictions from measurements of randomly positioned sensors. Statistical comparison proved that the developed methods and algorithms allow us to significantly reduce the prediction errors. |
| format | Article |
| id | doaj-art-119c986b04cb4e8490880575bc9efc24 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-119c986b04cb4e8490880575bc9efc242025-08-20T03:50:17ZengMDPI AGApplied Sciences2076-34172025-07-011513746310.3390/app15137463Optimization of Sensor Positions and Orientations for Multiple Load Case ScenariosWacław Kuś0Waldemar Mucha1Iyasu Tafese Jiregna2Department of Computational Mechanics and Engineering, Silesian University of Technology, 44-100 Gliwice, PolandDepartment of Computational Mechanics and Engineering, Silesian University of Technology, 44-100 Gliwice, PolandDepartment of Computational Mechanics and Engineering, Silesian University of Technology, 44-100 Gliwice, PolandThis paper focuses on optimizing sensor placement in structures for load monitoring applications. Such applications rely on sensor data to track changes in the structure. Monitoring accuracy relies on proper sensor placement. The goal is to maximize load monitoring accuracy under multiple loading scenarios while the number of sensors is kept smaller than the number of load cases. Building on prior work in which machine learning models predicted loads using only sensor readings without information on load location, this study continues that approach. It demonstrates that prediction models perform better when sensor networks are optimized. The novelty lies in a newly formulated objective function, allowing for optimization of sensor number, positions, and orientations across multiple load cases and measurement types. The goal is to minimize the differences between maximal responses of the structure and detected responses by the sensors (for all possible load cases). The method is validated on a numerical model of a composite structure with 1–3 sensors under seven different load cases. Load predictions from sensors in optimized locations are compared to predictions from measurements of randomly positioned sensors. Statistical comparison proved that the developed methods and algorithms allow us to significantly reduce the prediction errors.https://www.mdpi.com/2076-3417/15/13/7463optimizationsensor placementstructural health monitoringfinite element methodgenetic algorithmartificial neural network |
| spellingShingle | Wacław Kuś Waldemar Mucha Iyasu Tafese Jiregna Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios Applied Sciences optimization sensor placement structural health monitoring finite element method genetic algorithm artificial neural network |
| title | Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios |
| title_full | Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios |
| title_fullStr | Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios |
| title_full_unstemmed | Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios |
| title_short | Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios |
| title_sort | optimization of sensor positions and orientations for multiple load case scenarios |
| topic | optimization sensor placement structural health monitoring finite element method genetic algorithm artificial neural network |
| url | https://www.mdpi.com/2076-3417/15/13/7463 |
| work_keys_str_mv | AT wacławkus optimizationofsensorpositionsandorientationsformultipleloadcasescenarios AT waldemarmucha optimizationofsensorpositionsandorientationsformultipleloadcasescenarios AT iyasutafesejiregna optimizationofsensorpositionsandorientationsformultipleloadcasescenarios |