A sensor placement approach using multi-objective hypergraph particle swarm optimization to improve effectiveness of structural health monitoring systems

Muhammad Waqas, Latif Jan, Mohammad Haseeb Zafar, Raheel Hassan, Rameez Asif

Research output: Contribution to journalArticlepeer-review

6 Downloads (Pure)

Abstract

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.
Original languageEnglish
Article number1423
JournalSensors
Volume24
Issue number5
Early online date22 Feb 2024
DOIs
Publication statusPublished - Mar 2024

Keywords

  • Fuzzy Decision Making
  • Grey Relational Analysis
  • Multi-Objective Hypergraph Particle Swarm Optimization
  • Optimal Sensor Placement
  • structural health monitoring

Cite this