Hybrid Sensor Placement Framework Using Criterion-Guided Candidate Selection and Optimization
This study presents a hybrid sensor placement methodology that combines criterion-based candidate selection with advanced optimization algorithms. Four established selection criteria—modal kinetic energy (MKE), modal strain energy (MSE), modal assurance criterion (MAC) sensitivity, and mutual inform...
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2025-07-01
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| author | Se-Hee Kim JungHyun Kyung Jae-Hyoung An Hee-Chang Eun |
| author_facet | Se-Hee Kim JungHyun Kyung Jae-Hyoung An Hee-Chang Eun |
| author_sort | Se-Hee Kim |
| collection | DOAJ |
| description | This study presents a hybrid sensor placement methodology that combines criterion-based candidate selection with advanced optimization algorithms. Four established selection criteria—modal kinetic energy (MKE), modal strain energy (MSE), modal assurance criterion (MAC) sensitivity, and mutual information (MI)—are used to evaluate DOF sensitivity and generate candidate pools. These are followed by one of four optimization algorithms—greedy, genetic algorithm (GA), particle swarm optimization (PSO), or simulated annealing (SA)—to identify the optimal subset of sensor locations. A key feature of the proposed approach is the incorporation of constraint dynamics using the Udwadia–Kalaba (U–K) generalized inverse formulation, which enables the accurate expansion of structural responses from sparse sensor data. The framework assumes a noise-free environment during the initial sensor design phase, but robustness is verified through extensive Monte Carlo simulations under multiple noise levels in a numerical experiment. This combined methodology offers an effective and flexible solution for data-driven sensor deployment in structural health monitoring. To clarify the rationale for using the Udwadia–Kalaba (U–K) generalized inverse, we note that unlike conventional pseudo-inverses, the U–K method incorporates physical constraints derived from partial mode shapes. This allows a more accurate and physically consistent reconstruction of unmeasured responses, particularly under sparse sensing. To clarify the benefit of using the U–K generalized inverse over conventional pseudo-inverses, we emphasize that the U–K method allows the incorporation of physical constraints derived from partial mode shapes directly into the reconstruction process. This leads to a constrained dynamic solution that not only reflects the known structural behavior but also improves numerical conditioning, particularly in underdetermined or ill-posed cases. Unlike conventional Moore–Penrose pseudo-inverses, which yield purely algebraic solutions without physical insight, the U–K formulation ensures that reconstructed responses adhere to dynamic compatibility, thereby reducing artifacts caused by sparse measurements or noise. Compared to unconstrained least-squares solutions, the U–K approach improves stability and interpretability in practical SHM scenarios. |
| format | Article |
| id | doaj-art-e0bfea34924946419103b5697ef76bbf |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-e0bfea34924946419103b5697ef76bbf2025-08-20T02:47:10ZengMDPI AGSensors1424-82202025-07-012514451310.3390/s25144513Hybrid Sensor Placement Framework Using Criterion-Guided Candidate Selection and OptimizationSe-Hee Kim0JungHyun Kyung1Jae-Hyoung An2Hee-Chang Eun3Department of Architectural Engineering, Kangwon National University, Chuncheon 24341, Republic of KoreaDepartment of Architectural Engineering, Kangwon National University, Chuncheon 24341, Republic of KoreaDepartment of Architectural Engineering, Kangwon National University, Chuncheon 24341, Republic of KoreaDepartment of Architectural Engineering, Kangwon National University, Chuncheon 24341, Republic of KoreaThis study presents a hybrid sensor placement methodology that combines criterion-based candidate selection with advanced optimization algorithms. Four established selection criteria—modal kinetic energy (MKE), modal strain energy (MSE), modal assurance criterion (MAC) sensitivity, and mutual information (MI)—are used to evaluate DOF sensitivity and generate candidate pools. These are followed by one of four optimization algorithms—greedy, genetic algorithm (GA), particle swarm optimization (PSO), or simulated annealing (SA)—to identify the optimal subset of sensor locations. A key feature of the proposed approach is the incorporation of constraint dynamics using the Udwadia–Kalaba (U–K) generalized inverse formulation, which enables the accurate expansion of structural responses from sparse sensor data. The framework assumes a noise-free environment during the initial sensor design phase, but robustness is verified through extensive Monte Carlo simulations under multiple noise levels in a numerical experiment. This combined methodology offers an effective and flexible solution for data-driven sensor deployment in structural health monitoring. To clarify the rationale for using the Udwadia–Kalaba (U–K) generalized inverse, we note that unlike conventional pseudo-inverses, the U–K method incorporates physical constraints derived from partial mode shapes. This allows a more accurate and physically consistent reconstruction of unmeasured responses, particularly under sparse sensing. To clarify the benefit of using the U–K generalized inverse over conventional pseudo-inverses, we emphasize that the U–K method allows the incorporation of physical constraints derived from partial mode shapes directly into the reconstruction process. This leads to a constrained dynamic solution that not only reflects the known structural behavior but also improves numerical conditioning, particularly in underdetermined or ill-posed cases. Unlike conventional Moore–Penrose pseudo-inverses, which yield purely algebraic solutions without physical insight, the U–K formulation ensures that reconstructed responses adhere to dynamic compatibility, thereby reducing artifacts caused by sparse measurements or noise. Compared to unconstrained least-squares solutions, the U–K approach improves stability and interpretability in practical SHM scenarios.https://www.mdpi.com/1424-8220/25/14/4513sensoroptimal sensor placementstructural health monitoringJaccard similarity indexmodal assurance criterionfrequency response function |
| spellingShingle | Se-Hee Kim JungHyun Kyung Jae-Hyoung An Hee-Chang Eun Hybrid Sensor Placement Framework Using Criterion-Guided Candidate Selection and Optimization Sensors sensor optimal sensor placement structural health monitoring Jaccard similarity index modal assurance criterion frequency response function |
| title | Hybrid Sensor Placement Framework Using Criterion-Guided Candidate Selection and Optimization |
| title_full | Hybrid Sensor Placement Framework Using Criterion-Guided Candidate Selection and Optimization |
| title_fullStr | Hybrid Sensor Placement Framework Using Criterion-Guided Candidate Selection and Optimization |
| title_full_unstemmed | Hybrid Sensor Placement Framework Using Criterion-Guided Candidate Selection and Optimization |
| title_short | Hybrid Sensor Placement Framework Using Criterion-Guided Candidate Selection and Optimization |
| title_sort | hybrid sensor placement framework using criterion guided candidate selection and optimization |
| topic | sensor optimal sensor placement structural health monitoring Jaccard similarity index modal assurance criterion frequency response function |
| url | https://www.mdpi.com/1424-8220/25/14/4513 |
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