Pareto-Based Bi-Objective Optimization Method of Sensor Placement in Structural Health Monitoring
| Autor: | Dong-Hui Yang, Shao-Xiao Nong, Ting-Hua Yi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Mathematical optimization
Building construction Series (mathematics) structural health monitoring Pareto optimization Computer science optimal sensor placement bi-objective optimization Pareto principle Process (computing) Sorting Truss Building and Construction Multi-objective optimization Architecture Genetic algorithm non-dominated sorting genetic algorithm Structural health monitoring TH1-9745 Civil and Structural Engineering |
| Zdroj: | Buildings Volume 11 Issue 11 Buildings, Vol 11, Iss 549, p 549 (2021) |
| ISSN: | 2075-5309 |
| DOI: | 10.3390/buildings11110549 |
| Popis: | For a practical structural health monitoring (SHM) system, the traditional single objective methods for optimal sensor placement (OSP) cannot always obtain the optimal result of sensor deployment without sacrificing other targets, which creates obstacles to the efficient use of the sensors. This study mainly focuses on establishing a bi-objective optimization method to select the sensor placement positions. The practical significance of several single-objective criteria for OSP is firstly discussed, based on which a novel bi-objective optimization method is proposed based on the Pareto optimization process, and the corresponding objective functions are established. Furthermore, the non-dominated sorting genetic algorithm is introduced to obtain a series of the Pareto optimal solutions, from which the final solution can be determined based on a new defined membership degree index. Finally, a numerical example of a plane truss is applied to illustrate the proposed method. The Pareto optimization-based bi-objective OSP framework presented in this study could be well suited for solving the problem of multi-objective OSP, which can effectively improve the efficiency of the limited sensors in SHM system. |
| Databáze: | OpenAIRE |
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