A Sensor Placement Approach Using Multi-Objective Hypergraph Particle Swarm Optimization to Improve Effectiveness of Structural Health Monitoring Systems
In this paper, a novel Multi-Objective Hypergraph Particle Swarm Optimization (MOHGPSO) algorithm for structural health monitoring (SHM) systems is considered. This algorithm autonomously identifies the most relevant sensor placements in a combined fitness function without artificial intervention. T...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/5/1423 |
| _version_ | 1827319370155556864 |
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| author | Muhammad Waqas Latif Jan Mohammad Haseeb Zafar Syed Raheel Hassan Rameez Asif |
| author_facet | Muhammad Waqas Latif Jan Mohammad Haseeb Zafar Syed Raheel Hassan Rameez Asif |
| author_sort | Muhammad Waqas |
| collection | DOAJ |
| description | In this paper, a novel Multi-Objective Hypergraph Particle Swarm Optimization (MOHGPSO) algorithm for structural health monitoring (SHM) systems is considered. This algorithm autonomously identifies the most relevant sensor placements in a combined fitness function without artificial intervention. The approach utilizes six established Optimal Sensor Placement (OSP) methods to generate a Pareto front, which is systematically analyzed and archived through Grey Relational Analysis (GRA) and Fuzzy Decision Making (FDM). This comprehensive analysis demonstrates the proposed approach’s superior performance in determining sensor placements, showcasing its adaptability to structural changes, enhancement of durability, and effective management of the life cycle of structures. Overall, this paper makes a significant contribution to engineering by leveraging advancements in sensor and information technologies to ensure essential infrastructure safety through SHM systems. |
| first_indexed | 2024-04-25T00:20:12Z |
| format | Article |
| id | doaj.art-856dbe74a0d14381826c390e8b80bad6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-25T00:20:12Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-856dbe74a0d14381826c390e8b80bad62024-03-12T16:54:42ZengMDPI AGSensors1424-82202024-02-01245142310.3390/s24051423A Sensor Placement Approach Using Multi-Objective Hypergraph Particle Swarm Optimization to Improve Effectiveness of Structural Health Monitoring SystemsMuhammad Waqas0Latif Jan1Mohammad Haseeb Zafar2Syed Raheel Hassan3Rameez Asif4Electrical Engineering Department, Iqra National University, Peshawar 25000, PakistanComputer Science Department, Iqra National University, Peshawar 25000, PakistanCardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UKSchool of Computing Sciences, University of East Anglia, Norwich NR4 7TJ, UKSchool of Computing Sciences, University of East Anglia, Norwich NR4 7TJ, UKIn this paper, a novel Multi-Objective Hypergraph Particle Swarm Optimization (MOHGPSO) algorithm for structural health monitoring (SHM) systems is considered. This algorithm autonomously identifies the most relevant sensor placements in a combined fitness function without artificial intervention. The approach utilizes six established Optimal Sensor Placement (OSP) methods to generate a Pareto front, which is systematically analyzed and archived through Grey Relational Analysis (GRA) and Fuzzy Decision Making (FDM). This comprehensive analysis demonstrates the proposed approach’s superior performance in determining sensor placements, showcasing its adaptability to structural changes, enhancement of durability, and effective management of the life cycle of structures. Overall, this paper makes a significant contribution to engineering by leveraging advancements in sensor and information technologies to ensure essential infrastructure safety through SHM systems.https://www.mdpi.com/1424-8220/24/5/1423structural health monitoringMulti-Objective Hypergraph Particle Swarm OptimizationOptimal Sensor PlacementGrey Relational AnalysisFuzzy Decision Making |
| spellingShingle | Muhammad Waqas Latif Jan Mohammad Haseeb Zafar Syed Raheel Hassan Rameez Asif A Sensor Placement Approach Using Multi-Objective Hypergraph Particle Swarm Optimization to Improve Effectiveness of Structural Health Monitoring Systems Sensors structural health monitoring Multi-Objective Hypergraph Particle Swarm Optimization Optimal Sensor Placement Grey Relational Analysis Fuzzy Decision Making |
| title | A Sensor Placement Approach Using Multi-Objective Hypergraph Particle Swarm Optimization to Improve Effectiveness of Structural Health Monitoring Systems |
| title_full | A Sensor Placement Approach Using Multi-Objective Hypergraph Particle Swarm Optimization to Improve Effectiveness of Structural Health Monitoring Systems |
| title_fullStr | A Sensor Placement Approach Using Multi-Objective Hypergraph Particle Swarm Optimization to Improve Effectiveness of Structural Health Monitoring Systems |
| title_full_unstemmed | A Sensor Placement Approach Using Multi-Objective Hypergraph Particle Swarm Optimization to Improve Effectiveness of Structural Health Monitoring Systems |
| title_short | A Sensor Placement Approach Using Multi-Objective Hypergraph Particle Swarm Optimization to Improve Effectiveness of Structural Health Monitoring Systems |
| title_sort | sensor placement approach using multi objective hypergraph particle swarm optimization to improve effectiveness of structural health monitoring systems |
| topic | structural health monitoring Multi-Objective Hypergraph Particle Swarm Optimization Optimal Sensor Placement Grey Relational Analysis Fuzzy Decision Making |
| url | https://www.mdpi.com/1424-8220/24/5/1423 |
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