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<p>Structural health monitoring (SHM) involves systematic observation of structures to detect potential changes and assess their current condition. The effectiveness of SHM systems strongly depends on the number and spatial distribution of sensors. While sensor configurations can be determined based on engineering experience for small-scale structures, this approach becomes impractical for complex and large-scale structural systems. Therefore, this study proposes an optimization framework for determining the optimal sensor placement (OSP) of uniaxial sensors in two- and three-dimensional structural systems. Unlike conventional OSP approaches, this work makes a substantive contribution to the literature by introducing an automated computational environment that unifies MATLAB and the SAP2000 Open Application Programming Interface (OAPI). Furthermore, it optimizes both sensor numbers and locations based on modal mass participation ratios, eliminating human-induced bias. The Rao-1 algorithm is employed as a representative parameter-free metaheuristic to demonstrate the general applicability and robustness of the proposed framework, rather than to claim superiority of a specific optimization algorithm. In the proposed framework, the design variables of the optimization problem correspond to the sensor locations selected from the available degrees of freedom (DOFs) of the structure. The objective functions are formulated using max-MAC, avg-MAC, and rms-MAC criteria derived from the modal assurance criterion (MAC). The effectiveness of the proposed framework is demonstrated using two benchmark space-frame structures with different complexities (4-story and 20-story models). The results show that the proposed framework can significantly reduce the number of required sensors while maintaining high modal observability. Furthermore, multiple optimal sensor configurations may exist for the same objective value, highlighting the non-uniqueness of the OSP problem and providing flexibility for practical SHM applications.</p>

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A unified optimization framework for optimal sensor placement in 3D space frame structures based on MAC criteria and a metaheuristic algorithm

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • Büşra Yakak Vatandaş,
  • Barbaros Atmaca

摘要

Structural health monitoring (SHM) involves systematic observation of structures to detect potential changes and assess their current condition. The effectiveness of SHM systems strongly depends on the number and spatial distribution of sensors. While sensor configurations can be determined based on engineering experience for small-scale structures, this approach becomes impractical for complex and large-scale structural systems. Therefore, this study proposes an optimization framework for determining the optimal sensor placement (OSP) of uniaxial sensors in two- and three-dimensional structural systems. Unlike conventional OSP approaches, this work makes a substantive contribution to the literature by introducing an automated computational environment that unifies MATLAB and the SAP2000 Open Application Programming Interface (OAPI). Furthermore, it optimizes both sensor numbers and locations based on modal mass participation ratios, eliminating human-induced bias. The Rao-1 algorithm is employed as a representative parameter-free metaheuristic to demonstrate the general applicability and robustness of the proposed framework, rather than to claim superiority of a specific optimization algorithm. In the proposed framework, the design variables of the optimization problem correspond to the sensor locations selected from the available degrees of freedom (DOFs) of the structure. The objective functions are formulated using max-MAC, avg-MAC, and rms-MAC criteria derived from the modal assurance criterion (MAC). The effectiveness of the proposed framework is demonstrated using two benchmark space-frame structures with different complexities (4-story and 20-story models). The results show that the proposed framework can significantly reduce the number of required sensors while maintaining high modal observability. Furthermore, multiple optimal sensor configurations may exist for the same objective value, highlighting the non-uniqueness of the OSP problem and providing flexibility for practical SHM applications.